Top 10 Data Science Leaders of 2021

OCTOBER 2021

1

Data Science in

T O P

2021: What is the Future of this

DATA SCIENCE

Technology?

LEADERS OF

2021

What Requirements Can Make You a Perfect Data Science Leader?

VENKATA DUVVURI A VERSATILE LEADER DEPLOYING

SUCCESSFUL AI MODELS TO INNOVATE MARKETING AND CLOUD

Director of Data Science at Oracle

Data Science Leaders are Ruling the Corporate Industry for their Technical Talent as well as Management Expertise Data is running through the veins of advanced technology these days. The whole concept of algorithms and artificial intelligence is standing upon the concept of feeding a machine with stacks of data like the information stacked in our brain and then using that data for various operations, just how we use our brain to operate. The idea is very simple to grasp but it has a complex functioning structure that comes under the subject of data science.

The data science industry of 2021 is witnessing some of the biggest data science leaders of the decade. 2021 will be a pivotal year for data scientists as organizations are increasingly relying on insights derived from big data for making key decisions. More applications are being created with Python and there's an increased demand for end-to-end AI solutions. There are a lot of jobs available in the field, however, not enough data scientists. According to an ASA Report, nearly 70% of business owners prefer job applicants with specializations in data science and the number of job openings are projected to grow to 2.72 million by 2021.

We see colossal impacts of data science across businesses; however, some are more developed than others, especially in finance. We see enormous improvement being made and this is to a great extent is in light of the fact that these organizations have a ton of data as of now. Like finance has a long history of making data helpful, thus there is now a culture of being reasonably data-driven set up in a large number of these organizations, and they're additionally keen on stretching out those capabilities to new sorts of information.

Data science leaders are the senior executives with the aptitude to harness data science and business analytic insights to inform business decisions, strategy, execution, and more effective organizational leadership. Executives need to possess this critical and evolving knowledge foundation to optimally leverage the information produced for and by their data science teams in delivering business solutions based on analytic insights.

Data science is working pretty intensely in the media also. That is things like understanding your crowd, helping them discover content they'll cherish, helping them draw in with that content, ensuring it's shared ideally across various platforms. It's one spot, however extremely truly distributed. The exponential growth in data we have seen since the start of our digital period will back off at any point soon. Truth be told, we have most likely just observed a hint of something larger. The coming years will realize a consistently expanding downpour of information. The new information will work as rocket fuel for our data science models, offering rise to better models as well as new and imaginative use cases.

Most organizations hiring data science leaders generally look for a Ph.D. in a related field or with significant experience with machine learning models, and they often drop the management experience requirement to obtain technical talent and it may hamper the operation of a data science leader. The management experience would help the leader connect with the non-technical staff of his company.

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08

COVER STORY

Venkata

Duvvuri Oracle

16

24

Alberto Pascual

36

42

Tim Kao

28

Carlos Prades

Benjamin Tengelsen

56

50

62

Farzana Noorzay

20

ARTICLES

Mihir Kittur

Imre Szücs

Ravi Korlimarla

48

Data Science in 2021:

What Requirements Can

What is the Future of

Make You a Perfect Data

this Technology?

Science Leader?

VENKATA DUVVURI

08

COVER STORY

A Versatile Leader Deploying Successful AI Models to Innovate Marketing and Cloud

09

Data science is like taking the new oil “data” and making sense with machine learning and artificial intelligence to enhance the lives of humans and all beings in our beautiful planet and beyond.

Venkata Duvvuri Director of Data Science Oracle www.oracle.com

10

V

enkata Duvvuri is the Director of data science

Innovation.

at Oracle specializing in machine learning and

Venkata admitted that he is fortunate to experience

data science. Venkata is the leader of results-

management of onshore and offshore teams as well as to

oriented data science, business, web and marketing

gain leadership experience ranging from different roles

analytics with over 10 years of experience in data

from the Director to the Manager at Fortune 100

science, business analytics, digital media analytics and

companies including marketing agencies. This moulded

web marketing optimization. Besides Oracle, he is also an

him into a cross-functional leader and subject matter

adjunct faculty member at Northeastern University

expert in machine learning. He had the opportunity to

teaching data analytics and machine learning in his free

drive improvements in business and online marketing

time. In addition, he serves as the Board of Data Science

outcomes to the tunes of several million dollars at Oracle,

Advisors at a couple of startups, namely CIPIO and

eBay (triad retail media), Conversant (alliance data),

AIClub. Venkata also has over 10 years of software

AT&T (cricket wireless) and crunch several terabytes of

engineering experience and an MBA degree from the

data at Apple Corporation.

University of California. He holds a master's in computer

At Oracle, Venkata has innovated several AI features in

science from the University of Massachusetts. He is

CX marketing products like Responsys and Eloqua. As a

currently pursuing his doctorate at Purdue University in

result, he has filed 8+ patents and published 3 papers in

the Department of Technology Leadership and

reputed conferences in ACM and Springer.

Experiences that Shaped Him as Leader Venkata explained that during his early career, not being

initially he found marketing to be more of an art, and his

able to relate technology to business was a key problem.

mission was to change it to a science.

He was always an application-oriented engineer ready to

The right mix of marketing applications helped Venkata

meet business needs. Eventually, he learnt the craft and

explore verticals like retail and gaming. Ultimately, he

thereby, he additionally excelled in a graduate business

found the Oracle CX marketing to be satisfying, as he

degree at UC Davis and got elected to the Beta Gamma

could now solve for all verticals' needs. Now, the

Sigma society due to academic excellence. After

challenge for him as a leader is to innovate quickly as the

completing his MBA, Venkata changed into analytics and

AI space is moving along briskly.

focused on marketing applications. But once again

11

Hallmarks for Every Data Science Leader According to Venkata, data science is part research and

data science programs, the market gap between the

part engineering. Merging the two is a challenge for

supply of data scientists and openings is narrowing.

every data science leader. The talent is also isolated in

Hence, the leaders should not shy away from data

disparate buckets, but nowadays with the explosion of

science initiatives.

Fundamental Innovation is the Key to Success "Innovation can be either fundamental or applied" is

technologies are ever-changing. Hence, Venkata feels

what the Venkata believes. Venkata admitted that he has

that there is always an opportunity for more

always focused on applied research i.e. making

improvement in any field due to AI. The world is in an

fundamental innovation count in the real world. This

explosive AI growth phase and the moon is the next stop

gave him thrills and he enjoyed when the customer's

for AI humanoids.

experience was enhanced. Secondly, fundamental

12

Harnessing the Most Out of Disruptive Technologies For Venkata, AI/ML are the key to mining tremendous

Venkata asserted that Oracle has come up with a CX

data that is getting generated every second. “To look at it

marketing solution that is available to launch cross-

from my perspective, since 2013, I've been a leader and

channel marketing programs and unify all prospect and

Director at several marketing analytics organizations.

customer marketing signals in a single view. By bringing

Thats when I noticed that the pace of innovation is ever-

together human, machine, and domain data, digital

changing. Earlier consumers were skeptical of AI, but

subscription organizations can better understand their

now it's embedded every day in their lives. Leadership

subscribers, enhance subscriber experiences and grow

has also changed i that they now see needs of the Chief

revenue. Therefore, Venkata believes that making the

Data Scientist or Chief Data Officer at par with Chief

utmost use of disruptive technologies would yield

Operators. So, the sky is the limit for any leader to

extreme benefits in all sectors.

increase their contributions”, he added.

The Future of SaaS and Communications Venkata stated, "Oracle is a leader in marketing cloud. Its

more efficient. Gone will be days when emails, SMS and

leadership is unfettered despite competition. The Oracle

communications will be treated as spam. They will be

SaaS cloud industry will make the jobs of marketers even

efficient, personalized and to the point."

Words of Wisdom to the Budding Leaders Venkata breaks the stigma of learning by quoting

brave. So, take your chances and jump into this field with

“Continuous learning is the key to success. The human

vigour." He has also claimed that the AI enthusiasts

brain grows until 39 years old. So why stop learning at

should also take time to enjoy life as well. It's short and

21?". He says that, it's much easier to learn from many

one must make the best of it. He encourages the new

quality programs offered at throwaway prices. He has

leaders by saying, "You never know what you might be

advised emerging leaders to take advantage of this. He

called on to do next. AI/ML skills will help you in your next

says, "Try and try till you succeed. Luck only favors the

journey, possibly to a distant planet."

13

Venkata Duvvuri

1

Carlos Prades

Director of Data Science

Head of data science

Oracle

Antofagasta Minerals

T O P

Venkata Duvvuri is the Director of data science at Oracle, specializing in machine learning and data science. He is the leader of resultsoriented data science, business, web and marke ng analy cs, with over 10 years of experience in respec ve fields. Besides Oracle, Venkata is also an adjunct faculty member at Northeastern University teaching data analy cs and machine learning in his free me.

Carlos Prades is the Head of data science at Antofagasta Minerals. He pursued Geology at the University of Chile Engineering School, which gave him a strong mathema cal background. A er working for almost a decade, Carlos built a data science team in 2021 that developed several projects staging from explora on to the different steps of a mining opera on.

www.oracle.com

www.antofagasta.co.uk

Alberto Pascual

Mihir Kittur

Director: Data Science & Analy cs

Co-Founder & Chief Commercial Officer

IDBS

Ugam, a Merkle company

Alberto Pascual is a doctor in bioinforma cs with enhanced experience in data science and biomedical domains. He completed his bachelors in computer science and received a Ph.D. in bioinforma cs. At IDBS, Alberto is responsible for developing and execu ng the company’s strategy on AI, data science, and analy cs to deliver novel products and services for the customers in life sciences R&D markets.

Mihir Ki ur is the Co-Founder and Chief Commercial Officer of Ugam.

His interest in data science emerged from his engineering disserta on, which involved the usage of ar ficial neural networks to build a robo c arm. It further intensified during his s nt in management consul ng.

Later, Mihir started his entrepreneurship journey by working on several high-impact opportuni es with clients.

Szücs Imre is the CEO and CoFounder of op mAIze Marke ng Solu ons. He hails from the field of physics and astronomy, who later took a transi on into the IT and business world. In the first half of his career, Szücs worked as a data scien st who developed machine learning models and in the next half, he took up strategic posi ons such as a data science leader.

Farzana Noorzay is the Head of the data science, analy cs, engineering and data products teams at Tillster.

At the company, she is responsible for the data products including ac onable dashboards, which are used by decision-makers to define the course of ac on for promo ons based on consumer behavior trends.

Farzana enjoys being the head of products that affect clients as well as the restaurant consumers.

www.idbs.com

www.ugamsolu ons.com

www.nemze cegtar.hu

www. llster.com

Dean Abbott

Gideon Mann

DATA SCIENCE LEADERS OF

2021

Szücs Imre

Farzana Noorzay

CEO & Co-Founder

Head of the Data Science, Analy cs, Engineering & Data Products teams

op mAIze

Tillster, Inc.

Benjamin Tengelsen

Tim Kao

Data Science Director

Vice President of Data Science

Chief Data Scien st

Head of Data Science

IntelyCare

Center for Naval Analyses

SmarterHQ

Bloomberg

Dean Abbo is the Chief Data Scien st of SmarterHQ. Dean is an interna onally recognized data mining and predic ve analy cs expert with over two decades experience applying advanced data mining algorithms, data prepara on techniques, and data visualiza on methods to real-world problems. He is also the CoFounder and Chief Scien st of SmarterRemarketer, a company focusing on behaviorally- and datadriven marke ng a ribu on.

Gideon Mann is the Head of Data Science at Bloomberg, guiding the strategic direc on for machine learning, natural language processing, and search on the core terminal. At Bloomberg, his team has worked on the company-wide data science pla orm, natural language ques on answering, among other products. He also leads the Data for Good Exchange, an annual conference on data science applica ons for social good.

www.marterhq.com

www.bloomberg.com

Benjamin Tengelsen is the Data Science Director at IntelyCare. He leads a team of 10 amazing data analysts, scien sts, and engineers, who use machine learning, experimenta on, advanced sta s cs, and a whole lot of codes to support features in the apps related to pricing, recommenda ons, communica ons, customer service, recrui ng, and marke ng. Prior to this, Benjamin worked at Wayfair, where he focussed on product similarity and subs tutability. www.intelycare.com

Tim Kao is the Vice President of Data Science at Center for Naval Analyses (CNA). He has served in the United States Marine Corps for 20 years, including being a part of two combat tours to Iraq and Afghanistan. Tim started working at CNA in 2015 and is currently the Vice President of Data Science, where he leads over 25 data scien sts. www.cna.org

ALBERTO

PASCUAL

Director: Data Science and Analytics IDBS

16

Making the Maximum Use of Disruptive Technologies to Boost the Growth of Pharma and Other Industries

I

DBS helps research and development (R&D) teams

operations in 2003. The company received the Frost and

around the world to make discoveries that have the

Sullivan European Bioinformatics Project of the Year

potential to transform the lives of people worldwide.

Award for 2007. It was acquired by PerkinElmer in 2014,

The company's diverse customer list includes 22 of the

where Alberto had served as the Senior Manager for AI

top 25 global pharmaceutical companies and other R&D-

and analytics innovation. In his role, Alberto developed

driven organizations in the biotechnology, agricultural

analytics solutions in Translational, Screening, and

sciences, chemicals, consumer goods, energy, food and

Bioprocessing techniques. In September 2020, he joined

beverage, and healthcare, serving over 50,000

IDBS (Danaher group) as the Director of data science and

researchers in 25 countries.

analytics.

IDBS helps provide a foundation for the portfolio of life

At IDBS, Alberto is responsible for developing and

science informatics and knowledge management

executing the company's strategy on AI, data science,

solutions to accelerate the speed of discovering,

and analytics to deliver novel products and services for

developing, and producing new drugs and therapies.

the customers in life sciences R&D markets.

A Pioneering Leader

Learning from Life Experience

Alberto Pascual is a doctor in bioinformatics with

Alberto mentions that working for his own company

enhanced experience in data science and biomedical

shaped him as a successful data scientist. "Creating

domains. He completed his bachelors computer science

value is all you can do to survive in a very competitive

and received a Ph.D. in bioinformatics from the

market. Innovation is not an option; it is the secret recipe

Autonomous University of Madrid. Postdoc period, he

for success,” says Alberto. Talking about his another

joined the National Centre for Biotechnology (CNB-

relevant experience Albert feels that education and

CSIC), leading a bioinformatics research and core facility

teaching are interconnected. According to him, teaching

group. During that time, Alberto also co-founded

is one of the best ways to learn.

Integromics, a bioinformatics start-up that started its

17

I can foresee a few years from now, fully automated businesses using AI. Anything repetitive will be susceptible of automation. We should embrace this change. At IDBS we use this power to solve problems that a ect us, like developing new drugs.

Alberto Pascual Director: Data Science and Analytics IDBS www.idbs.com

18

Being Rigorous in Analysis is the Only Way to Survive

happens. From ideation to delivery, involving multiple

Perhaps the most significant challenge, according to

teams and experts in the development and

Alberto, was related to communication. He mentions

transformation processes is the key to innovation.

that he faced a lot of difficulties to determine how to transmit the right messages to the customers or users

Leadership Mantra: Always Stay Up to Date in

while dealing with complex problems in data science and

Technology

analytics simultaneously. Alberto adds that as a data

Technologies are always disruptive because they evolve

scientist, one must be rigorous in analysis and be flexible

fast. Alberto affirms that what is a trending topic today

communication. He also claims that both statistical and

can easily get old tomorrow and hence he asks the data

mathematical concepts are not always easy topics to

scientist leaders to stay up to date by finding the right

share and he hence advises to have patience, curiosity

balance in the adoption and usage of technologies. “It is a

towards education to improve master the subjects.

reality that data is driving all decisions nowadays, and to be competitive, leaders need to understand and use the

Patience at the Core of Data Science Leadership

data to get insights and knowledge, which necessarily

Alberto acknowledges that a perfect data science leader

needs using all tools in their hands, hire and retain the

must be able to understand the science and the domain

best talent and adopt technologies,” says Alberto while

by being patient and also by being a good listener. He

talking about evolution.

mentions, that “working hard for what one believes is correct, even if the results are not always pleasant.”

Heading Towards an Advanced Analytics Future

Alberto further adds on that it is crucial to educate the

Alberto strongly believes that IDBS will become an

younger generations about values and ethics because

industry leader in democratizing advanced analytics in

technology-wise new generations are already well

the pharma R&D space by providing and automating

educated and sophisticated.

better decision making. The company will definitely be

He highlights, “Work for them, not for you. Work for your

recognized as the best-in-class analytics tool.

customers. Focus on the business problem and not on the technical solution."

A Word of Wisdom for Upcoming Leaders Lastly, Alberto conveys a strong message for aspiring

Unleashing Customer-Centric Innovation

tech leaders and executives. He says, "Focus on what is

Alberto declares that listening to customers and

important: your customers, their data, and their

focusing on the business problem is everything one

problems. Use your tools wisely. Take care of your teams,

needs for innovation. He believes that technical

let them grow, create the talent."

knowledge and technology access is somehow easier to get, but how and when to apply it is when the innovation

19

DATA

SCIENCE IN 2021 WHAT IS THE FUTURE OF THIS TECHNOLOGY?

W

hether it's healthcare or hospitality big

investigation, exploring patterns and reports from web-

data is dominating every sector. Due to the

based media searches and engagements. Data

digital platforms, smartphones and IoT

researchers separate the volumes of information that

individuals would now be able to produce massive

show up into observable metrics and sort out things like

information than they might have envisioned a decade

where the changes are occurring the most, the sort of

ago. Cloud merchant Domo assessed that the normal

content clients routinely interface with, and more.

individual in 2020 created about 1.7MB of information consistently! Therefore there will always be more data

Data Science in Manufacturing

than you know what to do within every industry and

The second business that is receiving colossal rewards

associations are making up for a lost time to use it to the

from information science is fabricating. Analysis of

full degree. For instance, the suggestion engine utilized

assembled information has revolutionized

by online businesses is one of where information is

manufacturing activities, cut down repetition, enhanced

utilized to dissect client conduct and fabricate proposal

creation rates, further developed yields in fabricated

diagrams to expand deals and give the purchasers what

merchandise, diminished blunders in inventory network

they need.

determining, and numerous different viewpoints identified with the business. Organizations utilizing

What is Trending in Data Science?

mechanization, information mining, and AI have supported their effectiveness including their upper hand

Business Insights from Big Data

and diminished production network hazard.

The information produced and stored for quite a long time and the information that is continually being caught

Real-Time Data Analysis

offer incredible business insights that assist associations

Medical diagnostics and the logistics industry are several

with working on their reach, enhance their processes and

industries that make good use of data analysis in real-

increment their profits on a venture. Marketing

time. With the help of collected and analyzed data, data

specialists can utilize information acquired through

scientists come up with accurate predictive models that

20

can be applied in real-time applications. In the hospital,

part of AI that can learn without any help and change

real-time data analysis can reduce the individual

business capacities through better information the

workloads of staff and nurses or become the difference

board and analytics. This will likewise let loose data

between life and death in special circumstances. On the

researchers deal with greater innovation like deep

other hand, in the logistics industry, real-time data

learning.

enhances predictive times for shipments, avoids delays and downtime on critical assets, and contributes to

The Tremendous Growth in Data Science Jobs

empowering the performance of vehicles by insights into

While IT-centered positions have been extremely

operation methods.

popular in the course of the most recent twenty years the pace of development in the area has been projected to be

What the Future is Expected to bring in Data Science?

about 13% by the Bureau of Labor Statistics. It is as yet

Now that you know the potential of data science beyond

higher than the normal pace of development for any

what is already being implemented, these are some of the

remaining areas. Notwithstanding, information science

things that can be expected from the future:

has seen a hazardous development of more than 650% since 2012 dependent on an investigation done on

Increased Adoption of Artificial Intelligence in Business

LinkedIn. The data scientist job is one of the hot jobs in

In the past few years, information mining and planning

the market and the demand for data scientists is

procedures took up a large part of the spotlight in the

constantly increasing in various sectors.

discipline insight gathered by those fundamentally

The popularity of data researchers comes from the need

further developed business choices. Nonetheless, they

of large organizations to dig their information for bits of

are nowhere close to the disruption that AI techniques

knowledge and process advancement at each level. The

are going to get in the forthcoming decade. Artificial

C-level chiefs are accountable for the IT dynamic spot

intelligence can amazingly work on the productivity of

data analytics and business intelligence. Subsequently,

organizations and their interaction and offer major

information abilities have been positioned as one of the

benefits in overseeing clients and customer information.

most pursued in all businesses.

Automated artificial intelligence models are the second

21

CARLOS

PRADES

Head of Data Science Antofagasta plc.

24

A Versatile Leader Deploying Successful AI Models to Innovate Marketing and Cloud

A

ntofagasta plc is a multinational company

In 2015, he went to Canada to achieve a Master's degree

which is one of the most prominent

in one of the best mining universities in the world, the

conglomerates of Chile with equity

University of Alberta. While applying machine learning

participation in Antofagasta Minerals, one of the major

methods to mining problems during his research, he

copper producers of the world, operating four copper

started competing in Kaggle, the most important

mines namely: Los Pelambres, Centinela, Antucoya, and

international platform for international challenges,

Zaldivar. Antofagasta plc also has equity participation in

where Carlos achieved three medals in four

the railroad from Antofagasta to Bolivia, Twin Metals in

competitions one each in gold, silver, and bronze.

Minnesota, and other exploration joint ventures in

He came back to Antofagasta Minerals at the end of 2017

different locations of the world. The company islisted on

and began to apply data science to different and diverse

the London Stock Exchange and is a constituent of the

mining problems, leading the data science path in the

FTSE 100 Index.

mining industry and demonstrating the power of machine learning techniques to make better decisions

The Journey Towards Building a Team

based on data. Carlos worked on his own and generated

Carlos Prades is the Head of data science at Antofagasta

value supporting decisions in some of the company's

Minerals. He pursued Geology at the University of Chile

mines for almost two years.

E n g i n e e r i n g S c h o o l , w h i c h g ave h i m a s t r o n g

In January 2020 he built a data science team that

mathematical background. After his studies Carlos

developed several projects staging from exploration to

worked for a few years as a mine geologist and as an

the different steps of a mining operation. Carlos has led

exploration geologist in Talcuna Mine and Escondida

the team, using creativity to add value in the mining

Mine (BHP Billiton). In 2012 Carlos began working at

industry using data science.

Antofagasta Minerals as a resource geologist, applying his geostatistical abilities to make 3D models and

A Leader with Passion

resource estimation of the company's deposits.

Carlos says that studying geology, working as a

25

Data is growing exponentially in the mining industry, but the ability to extract all its value is still in early stages. We need to apply diverse and sophisticated data science techniques to optimally benefit from the huge knowledge that data contains.

Carlos Prades Head of Data Science Antofagasta plc. www.antofagasta.co.uk

26

geologist, and pursuing MSc in mining engineering gave

science leader needs to possess to be successful are

him a strong mining and mathematical background.

creativity, motivation and the ability to learn by

During his master's in science research, he learnt about

themselves. He says that achieving practical and

machine learning. Carlos has explored a lot about data

economic results has a lot to do with research. Carlos

science in Kaggle, which allowed him to practice with

says that working when inspiration comes can motivate a

real problems and also learn from some of the best data

good data scientist to heights. For this reason, he thinks

scientists in the world.

that a data science leader must set goals but at the same

He has been able to demonstrate the power of applying

time has to give freedom to the team to work and get

data science to mining problems by developing

more inspired.

successful projects in the mining industry, which have

“I don't care about how many hours my team members

added a lot of value to the company which is formally

work nor when they work. I just care about them

estimated as several million dollars. Carlos also says

achieving the goals we have set. I think a good data

that the company is always developing projects in

science leader has to trust and give a lot of freedom to the

mining in which data science has not been applied

team members so they can work with lots of motivation

before. So, he helps Antofagasta Minerals in finding

and produce moments of great inspiration”, says Carlos.

ways to apply data science techniques in innovative ways to leverage mining.

Embracing Technological Tools While talking about disruptive technologies, Carlos says

An Opportunity to Demonstrate the Power of Data

that data science have demonstrated its value in

Science

different industries. He believes that leaders of the

When Carlos came back from Canada to Antofagasta

future have to embrace these powerful tools, instead of

Minerals after pursuing MSc in Mining Engineering, he

making decisions based on intuition or beliefs. Carlos

took his previous position in the company as a resource

asserts that they have to listen to what data says with an

geologist. But he wanted to apply machine learning as he

open mindset.

knows his power when dealing with complex and multivariate systems. For this Carlos had to persuade his

A Much-Anticipated Data Supported Future

current boss (Andronico Luksic, Antogasta Minerals

Carlos predicts the future decisions of the company will

Development Vicepresident) to give him a chance to

be made with supported data. The decisions that are

demonstrate the power of data science. Andronico

either made by the high executives or automated will be

trusted him and allowed him to apply his knowledge,

made taking into account the data and algorithms in the

which led to successful projects. The first important and

next coming years to come, he adds.

successful project that Carlos developed was in the Antucoya mine, in which he used data science to make

A Piece of Wisdom to Emerging Tech Leaders

decisions that led to increased copper recovery by

Carlos advises all the tech leaders to give opportunities

around 5%. After that Carlos went on to build the first

to people with demonstrated capability to learn by

data science team in Antofagasta Minerals which he is

themselves, to approach and solve problems even when

still leading.

they have no previous knowledge about it. Carlos asks young future tech leaders to build up confidence with

Creativity and Motivation as the Two Vital Attributes

intelligence that sharpens their ability to figure any

According to Carlos, the vital attributes that every data

problem out for a better growth of the organization.

27

MIHIR

KITTUR

Co-Founder and Chief Commercial O cer Ugam, a Merkle company 28

Driving Impactful Business Results with Analytics and Technology

U

gam, a Merkle company, is a leading analytics

worked with clients from across the globe for over 23

and technology services provider. The

years to understand their needs and leverage analytics

company's customer-centric approach

and technology to drive impact.

delivers impactful business results for large corporations

His interest in data science emerged from his

by leveraging data, technology, and expertise. Ugam's

engineering dissertation which involved the usage of

services include analytics, technology, digital commerce,

artificial neural networks to build a robotic arm. It

and market research. With 3300+ people spread across

further intensified during his stint in management

locations worldwide, the company deploys its services to

consulting at one of India's leading consulting firms.

create success stories across industries like retail and

Mihir's interest found a stronghold when he began his

consumer brands, high tech, BFSI, distribution, and

entrepreneurship journey by working on several high-

market research and consulting.

impact opportunities with clients.

A Passionate Leader

Having a Pragmatic & Beginner's Mindset

Mihir Kittur is the Co-Founder and Chief Commercial

Under Mihir's co-leadership, Ugam, certified as “Great

Officer of Ugam. He is responsible for business

Place to Work”, has grown to 3300+ people, spreading

development and customer success. Mihir holds a

across locations worldwide with a marquee list of clients.

Bachelor's degree in Electronics Engineering and a

His journey was shaped by countless lessons that he

Master's degree in Business Management. Mihir has

continues to practice himself and impart across the

29

The future of decision making is augmented intelligence. Bringing the right blend of 'human + machine' will be a key di erentiator.

Mihir Kittur Co-Founder and Chief Commercial O cer Ugam, a Merkle company www.ugamsolutions.com

30

organization.

ecosystem of knowledge sharing and upskilling at Ugam

Mihir believes that a beginner's mindset is a key

that comprise of both internal and external forums such

ingredient needed for driving innovation. “By setting

as the Ugam Customer Summit, Learning Thursdays,

aside our own expertise and experience, we are

client workshops, certifications, and more.

empowered to challenge the status quo and explore new possibilities,” he says.

Innovation that Starts with the Customer

Finally, for data science to be effective, Mihir asserts that

“Sometimes innovation begins with an idea. But in most

it is important to have a pragmatic mindset, which goes

cases, it begins with the customer,” says Mihir. At Ugam,

beyond simply having the 'coolest' model, to enabling the

he encourages the teams to stay tuned to the customer's

easy consumption and actionability of insights.

needs and expectations for timely and continuous innovation. He also stresses the importance of getting

Customer-Centricity at the Core of Data Science

multiple perspectives and knowledge sharing when it

Leadership

comes to understanding the customer's problem and

Mihir believes that one of the key attributes for any

ideating on an effective solution. He believes that it is

successful data science leader is customer-centricity. He

important to celebrate good ideas but, it is also as

encourages leaders to approach every customer with

important to know when to let go of them. He advises

“What are we trying to solve for?” and then leverage the

leaders to love their ideas, but never to fall in love with

necessary AI and analytics to solve it. At Ugam, he has

them.

been influential in driving superior business results by embedding this approach through multidisciplinary

Embracing Human Plus Augmented Intelligence for

teams that comprise of both soft skills like consulting and

Success

problem-solving and hard skills like statistical

The emergence of various new-age technologies is

techniques and technology.

accelerating innovation in today's world. In this context,

Another attribute that Mihir believes to be vital for data

Mihir remarks that the role of a data science leader is to

science leaders is fostering a continuous learning

help the clients create an ecosystem with the right

mindset. He has been instrumental in creating an

balance of 'human + machine' or augmented intelligence.

31

“While such technologies continue to exist and

the past few years, he opines that this is just the early

accelerate innovation, bringing human intelligence at its

stages of a deep, long-term change. He expresses that

centre enables one to be more mindful of the problem

this change was set into motion by the shift to digital in

they are trying to solve and achieve the best results” he

recent times and was further accelerated due to the

adds.

pandemic. “Data science is no longer a 'nice to have' but a 'must-have' capability for businesses to survive and

Appreciating the Value of Customer-Centric Thinking

thrive” Mihir further adds.

Like every other data science leader, Mihir has encountered challenges with siloed data, dirty data,

A Piece of Mihir's Mind for Upcoming Leaders

insufficient data, and so on over the years. However, he

Mihir advises that in order to truly innovate, one must

recounts that one of the key challenges he has had to

foster a beginners' mindset within themselves and

overcome is not fully appreciating the value of customer-

across the organization. He says that by setting aside

centric thinking. He champions the importance of

one's own expertise and experience, people are

approaching every problem by thinking not only from the

empowered to challenge the status quo and explore new

customer's perspective but also from that of the

possibilities. Secondly, Mihir insists to not underestimate

customer's customer. This approach will enable leaders

the power of storytelling in data science. He states that

to blend the correct understanding of the problem with

storytelling plays a key role in translating complex

the right amount of data science to solve it.

information into consumable insights in a manner that is both engaging and persuasive. Finally, Mihir wraps it up

Foreseeing a Deep and Long-Term Change in the

by quoting “Simply caring about the work you do will give

Business Landscape

you the boost needed to push boundaries, drive

Mihir foresees the tremendous potential for data science

innovation, and sustain long-term performance.”

in the years ahead. While data science has exploded over

32

TIM

KAO

Vice President of Data Science CNA

36

Infusing Data Science to Revolutionize the Functionalities of the Navy and Marine Corps

T

he Center for Naval Analyses (CNA), a Federally Funded Research and Development Center (FFRDC), is sponsored by the Department of the Navy and headquartered in Arlington, Virginia. CNA's mission is to help Navy and Marine Corps leaders ensure America's defense in the 21st century. CNA is unique because of its real-world, empirical, and data-driven approach to operations analyses and use of data scientists as expert observers of military operations.

CNA is the nation's oldest operations research organization with lineage tracing back to World War II (1942) when CNA scientists went to sea aboard Navy ships to observe and analyze anti-submarine warfare operations in order to help defeat German U-boats. Its research staff has diverse educational disciplines with nearly 70% with Ph.D. degrees and 92% having graduatelevel degrees.

A Visionary Leader with a Strong Background Tim Kao is the Vice President of Data Science, CNA. He has served in the United States Marine Corps for 20 years, including being a part of two combat tours to Iraq and one to Afghanistan. During those two decades, Tim served in artillery, special operations, space, jungle

warfare, and operations analysis commands. He received a B.S in Operations Research from the Air Force Academy, an MBA from the University of Colorado, and a Master's in Operations Research from the Naval Postgraduate School. Tim started working at CNA in 2015 and is currently the Vice President of Data Science.

As the VP, he has the privilege of leading over 25 data scientists that are collaborative partners with Navy and Marine Corps sponsors and committed to supporting America's naval services.

Relationships between variables and people Data scientists rightfully focus on relationships between features and variables. However, leadership, even for data scientists, is fundamentally is about relationships with people. As a young jumpmaster instructing parachuting at the Air Force Academy, Tim quickly learned that the safety and success of personnel learning to jump out of airplanes depended on the trust and relationship they had with him as their instructor. If that was lacking, the probability of having students that refused to jump increased significantly. In the Marines, Tim worked for some great leaders and some bad ones.

The difference between those two types of leaders came

37

Data scientists spend a lot of time on machine learning, but we also need to spend su cient time on human learning. We have to understand the needs of our customers and teammates so that we can provide optimal solutions.

Tim Kao Vice President of Data Science CNA www.cna.org

38

down to the relationships those leaders were able to establish with those people they led. Leading data scientists is no different. Tim values a data scientist's technical skills, but their ability to exhibit the extremely hard to master 'soft skills' is just as important. That is what CNA looks for when hiring data scientists (and they are hiring!) and it contributes to the team's success.

collaboration with others. Being caring is also vital because Tim believes in the truism that no one cares how much you know until they know how much you care. Tim asserts that “Leading data scientists really isn't much different than leading Marines in combat. You have to know yourself (what are your data science capabilities), know your enemy (what is the problem being solved), and take care of your troops (data scientists) so they want to do a good job for each other and you,”

Constantly learning: There is always something new to learn Tim presumes that the ever-evolving discipline of data science requires a constant refresh on emerging algorithms, technologies, and processes. He says that when he joined CNA, he was coming from a USMC assignment as the commander of the only jungle warfare training center in the Department of Defense as well as a recent deployment to Afghanistan. While Tim was able to use some of his data science knowledge to support those military units, many of my data science skills quickly atrophied, as he did not use them regularly.

According to him, overcoming this challenge required a growth mindset to catch up quickly on emerging machine-learning techniques and code. This continues to be a challenge today as new things in data science are released every day. “A growth mindset is critical for a data scientist for continued success,” highlights Tim.

Talented Data Scientists and Healthy Partnerships are at the Core of Business Development One of the was Tim addresses the needs of his customers is to apply agile and scrum techniques to help solve data science problems. At CNA, Tim uses concepts like developing minimum viable products (MVPs) to iterate with its Navy and Marine Corps project sponsors. These sponsors are an integral part of the process in the creation of any data science solution CNA is developing.

CNA not only works with them on the user interface, but collaborates with them to understand the data, how it is collected, what is missing, and assumptions data scientists need to make in order to create a useful model.

A successful data science effort requires both superb data scientists and a close partnership with the Navy and Marine Corps end-users of the analytics.

Curiosity and Caring Behavior: Two Secrets of a Successful Leader For Tim, the two most important characteristics data science leaders need to have are being curious and caring. He adds that being curious is important because leaders won't know everything, but need to know enough to search out an answer through research and

Facilitating New Learning Opportunities Tim is adamant that data scientists need to have opportunities to leverage disruptive technologies in their work. Like other professions, but perhaps even more so, data scientists want to do innovative and meaningful work. It's incumbent on data science leaders to provide that opportunity. He adds that even if a data

39

science team members think they are too busy doing work to learn new things, by definition, there is a major inherent risk from overlooking disruptive technologies.

That risk will manifest itself by the team not being very busy much longer as their work will go to people that have learned those new things.

Entwining Hard Skills and Soft Skills for Better Outcomes According to Tim, much like a rising tide lifts all boats, such is the case for many data science organizations in this current era of data science being very much in vogue. . The data science leaders that separate themselves in this type of environment are the ones that can couple the hard skills (i.e. statistics, programming, machine learning) with the soft skills (i.e. communication, empathy, collaboration). According to Tim, at CNA, the team works on both sets of skills every day so it can help provide a competitive advantage for America's defense.

Creating a Better Environment for Future Data Science Teams “Don't forget about your people” asserts Tim. According to Tim, most data scientists, and CNA is no different, spend more of its waking hours working than they spend with their families. Tim insists that the data scientists on his team do everything they can to help their colleagues look forward to coming to work every day. Tim asks them to create an environment that provides the psychological safety for people to share their ideas and problems. According to Tim, data scientists get better solutions and outcomes if they build an inclusive and learnable environment in their workplace, where their team will work as long and as hard as ittakes to

40

accomplish the mission and no longer and no harder. He concludes by saying, in the Marine Corps it is “Mission first, Marines always” and at CNA I've adopted that mantra to be “Mission first, People always.”

BENJAMIN TENGELSEN

Data Science Director IntelyCare

42

Empowering Nursing Professionals on a Global Scale by Integrating Technological Advancements

O

ver the past years, technological

work for the wages and benefits they deserve with the

advancements have transformed the medical

help of data and technology.

industry. The days of meticulous charting and

manually filling records are dwindling. The emerging

A Dynamic Leader

new technologies in healthcare industry led the patients

Benjamin Tengelsen is the Data Science Director at

to have a greater accessibility to data than ever before.

IntelyCare. He leads a team of 10 amazing data analysts,

Technology has become to be a blessing for improved

scientists, and engineers, who use machine learning,

healthcare services and resonating with this change,

experimentation, advanced statistics, and a whole lot of

IntelyCare has emerged as a platform that is

codes to support features in the apps related to pricing,

empowering nursing professionals by integrating

recommendations, communications, customer service,

technology.

recruiting, and marketing.

IntelyCare is the largest digital nurse staffing platform in

Benjamin worked at Wayfair, one of the largest e-

the U.S. The company's easy-to-use app matches nursing

commerce companies in the country where he focussed

professionals with open assignments at the post-acute

on projects related to product similarity and

facility partners through machine learning and data and

substitutability. Before that, he studied economics at

behavioural science. IntelyCare is leading the work

Carnegie Mellon University and most of his research

revolution for nurses by empowering them with the

dealt with labour market search models, which he still

freedom to choose when, where, and how often they

thinks about very often while considering how to remove

43

With companies like IntelyCare allowing nursing professionals to work when and where they want, traditional healthcare providers will have to adapt in big ways if they want to survive a global nursing shortage.

Benjamin Tengelsen Data Science Director IntelyCare www.intelycare.com

44

f r i c t i o n s f r o m t h e l a b o u r m a r ke t f o r n u r s i n g

Experimenting to Leverage Customer Satisfaction

professionals.

Benjamin says that IntelyCare keeps experimenting and also believes that there is no better way to know how

Learning the Most from Experiences

customers will react than by seeing their reactions. But

Benjamin shares, "During the winter, my older brother

designing an experiment for every new product can take

and I would go skiing together. He was a much better

a long time. The poor engineers have twice as much work

skier and my brother would take me to parts of the

to do when his team enters the room and suggests yet

mountain that were way above my comfort level. My

another A/B test. Hence Benjamin says that the company

whole body would hurt at the end of the day, but my

tries to build the products in a way that allows for quick

skiing improved quickly. From that and many similar

experimentation later which doesn't require a a lot of

experiences, I learned that it is ok to feel uncomfortable,

engineering support.

even stupid because, that is when you are learning the most."

Innovation Made Possible Through Disruption

He further says that at IntelyCare, he feels stupid all the

Talking about disruptive technologies, Benjamin

time, and it is wonderful. The data questions that are

mentions that he is a big fan of the recent advances in

dealt with are so difficult, and the company is growing so

cloud-based data warehousing. Companies like

fast. He says that the best path forward is rarely obvious,

Snowflake, Fivetran, DBT, and Hightouch have cut the

but the team is learning new things every day. Benjamin

time required to stand up a quality data warehouse from

feels fortunate to be surrounded by great people who

months to days and he believes that innovation is only

tolerate his stupidity and make the journey fun.

possible with disruption.

Crucial Qualities of Data Science Leaders

Words of Wisdom for Aspiring Leaders

Benjamin asserts that motivating others is one of the key

Benjamin advises the aspiring leaders to keep

attributes that leaders should possess. "My first

experimenting. Sit close to your product team! Your

manager at Wayfair had a sticky note on his computer

fancy algorithms are no good unless they make it into

that said something like- “Love the people you lead”. He

some usable product he adds."

was pretty good at it, he wanted everyone on the team to be successful more than he wanted success for himself.

A Flexible Future

He was generous with his time. As a result, his team was

IntelyCare is all about allowing nursing professionals to

highly motivated to help each other and delivered some

work when and where they want. Benjamin hopes that by

great projects” recollects Ben. Demonstrating this

giving nurses flexible outside options, the full-time

example, Benjamin stresses on teamwork and generosity

employers will follow suit and find ways to provide

to be the two most important qualities of a data science

nurses outside of IntelyCare with more flexibility as well.

leader.

“If someone wants to be a nurse between 9 am and 2 pm

Benjamin always believes in overcoming challenges with

when their kid is in school, let them! The US already fell

confidence. “People in academia talk about their work

short of 1 million nurses before covid, the industry has

very differently than people in business. An individual's

got to find new ways to engage any talent on the side lines

business colleagues t need not know 99% of what a data

claims Benjamin”.

scientist could possibly tell them. They only need to know how well a given model is performing” advices Benjamin.

45

WHAT REQUIREMENTS CAN MAKE YOU A PERFECT

DATA SCIENCE LEADER?

M

achine learning teams do not survive without skilled leaders. Pushing papers from one side of the desk to the other is what managing entails. The team is becoming more capable as a result of the leadership. Team productivity and project performance are held to a high standard. Leaders are in charge of developing relationships inside the team as well as with the rest of the company. The best teams do not necessarily have the best players. They have the most capable leaders. Mentoring and growth are essential elements in excellent leadership. Communication is essential. Leaders establish a team's identity and expectations. They take a group of people and put them together to form a cohesive unit that is more productive as a whole.

What doesn't work when it comes to leading a data science team?

Because we deny leaders their conventional sources of power, data scientists are difficult to lead. Add to that the difficulties of leading top achievers and the work's intricacy. Pop psychology and motivational posters are ineffective. Data scientists despise being in charge. Some have worked as consultants or in small groups. They've had a lot of autonomy and little to no supervision. Others have only worked with ineffectual leaders. They perceive the job as a roadblock to advancement. Some have worked under bosses that were dictatorial, dismissive, and mocked ideas that were not their own. In technical teams, competence is a frequent source of authority.

Because the team naturally follows the greatest engineer, he or she is frequently elevated to a leadership role. This holds for data science teams as well. A tiny portion of the total responsibility is technical leadership.

Even skill finally fails since someone more knowledgeable is always available. Machine learning is just too complicated for a single individual to be the most

48

knowledgeable on every project. One of the most important aspects of leading a data science team is the leader's ability to surrender technical leadership without losing authority.

Two effective authority sources Create a balance in which the data science team's actions benefit both the company and the data scientists.

Leaders must take the time to learn about each data scientist's objectives and motivators, and then design a framework that compensates them for actions that provide value to the company. Because the data scientist is intrinsically motivated by those layered goals, alignment is a strong source of authority. The second aspect of alignment is linking team goals to individual goals. To avoid the dreaded "Not My Project" mentality, data science should be a collaborative effort. It's all too tempting for data scientists to get caught up in their initiatives at the expense of others. Setting up a structure that rewards everyone for team achievement encourages everyone to pitch in when they're required.

Instead, then putting accountability on the leader, this paradigm promotes accountability for each other. This is how the second source of authority, trust, is established.

The foundation for the team to be a part of the broader organization and for each data scientist to be a part of the team is built around aligned goals. When everyone sees how the team and the broader company are working together to achieve the same goals, they are more likely to trust each other. When they believe, whether or wrongly, that a person or organization is not completely dedicated to those aims, they will be just as determined to pointing it out and demanding a solution. Only if the leader is willing to follow the team can he or she ask the team to follow them. Data scientists expect to be heard and their ideas to be implemented. As a result, a data science leader is frequently in charge of managing and

communicating with senior management. A data science leader must be capable of resisting deadlines, projects, a lack of funds or personnel, and any other circumstance in which the team is ordered to undertake work that does not align with their understanding of the goals. This gap necessitates a leader who is capable of addressing the problem at whatever level is required.

Mentoring and team development Leaders are in charge of increasing the team's capabilities. Their finest instruments for achieving that aim are hiring and mentoring. What kind of mentorship is required? Who should the team get on board? Is there a genuine need for fresh players on the team? People can develop throughout their lifetimes. Most people are discouraged from doing so, especially after they reach a senior position. It is self-evident that the discipline of machine learning requires ongoing education.

Mentoring gives you the chance, structure, and direction you need to flourish. The two major paths for professional development are career path and capabilities. Data scientists become bored and seek a more difficult task. Talented people are attracted to companies that are growing.

Most data scientists aspire to increase their business value and their capacity to deliver concrete results. A leader begins mentoring by demonstrating their abilities.

It's difficult to recognize your abilities. That candid selfevaluation typically needs the assistance of a mentor who can function as a mirror. What competencies does the company require in the next 6 to 18 months to accomplish the next cycle of projects? A leader gives orders in this manner. Show them what the company requires and let them select what interests them the most. Concentrate on enhancing their assets. A lot of data scientists want to be unicorns. Increasing a talent from beginner to expert is more beneficial than improving a skill from beginner to average.

The company, on the other hand, can only give so much opportunity. The interests and competence growth plans of a data scientist may not align with the demands of the company. Being a good mentor often leads to a bright data scientist leaving the firm, which puts a leader in a difficult situation. Leaders also mentor their successors as well as other mentors. The process of capability development is learned, apply, teach. Mentoring's long-

term goal is to educate them on how to mentor others.

Mentoring skills must be taught to senior team members.

Leaders must recruit substitutes and train others to do their duties. If you are the only one capable of leading the team, you will not be promoted.

New data scientists are being hired When a leader recognizes their potential to teach and provide a growth path for their team, the norms of recruiting are fundamentally altered. You don't need to recruit data scientists with a long list of qualifications.

The team isn't looking for the ideal applicant. A developing, cohesive team understands that they can educate a recruit a lot of what they need to contribute.

They have more faith in their abilities to get a recruit up to speed. Candidates with this mentality are more likely to be long-term contributors. Quick learners, ambition, solid intellectual underpinnings, and communication abilities are all sought by the team. You must also have a systematic hiring procedure as a leader. Inside and outside of the company, leaders' network. Internal promotions are the most effective way to fill new positions. Trying to screen someone during an interview is significantly less successful than building connections with possible recruits outside of the organization. A list of questions is provided for each stage of the interview process. Follow-up inquiries are permissible and can be more loosely framed. The procedure is well-defined, reproducible, and equitable. Each individual in the interview is given an equal amount of time to speak.

A leader needs a development plan and a mentor You might spend so much time focused on the team's progress that you forget about your own. Mentors are essential for leaders. Leaders require a career path and a plan for personal development. From team to team, organization to business unit to business, leadership develops. Leading and developing data science teams for a significant portion of your career may be quite gratifying. You'll still need a mentor since your leadership style and effectiveness can always be improved. The professional path of leadership might lead to the establishment of your own company. A mentor can also assist you in this area. A good mentor will often help you get over them. They'll prepare you to be better than they are.

49

IMRE

SZÜCS

CEO/Co-Founder optimAIze Marketing Solutions

50

Harnessing the Power of Machine Learning to Strategize Operational Excellence in the Digital World

o

operational excellence in the digital world by

and even in the large enterprises from scratch, all across

using the power of machine learning. This start-up is

Europe. Now he is the leader of optimAIze, where he is

focused on developing machine learning-based services

utilizing his experiences to achieve success from what he

to optimize the ROI of digital marketing activities.

learned in his journey. Besides that, Imre is also leading

ptimAIze is established with the primary goal

He is proud to have been able to set up the data and

o f h e l p i n g t h e c u s t o m e r s t o a c h i eve

analytics teams in different organizations like start-ups

the ML initiatives in a large Hungarian enterprise.

A Talented and Dedicated Leader Imre Szücs is the CEO and Co-Founder of optimAIze

Important Lessons that Laid a Pavement to Success

Marketing Solutions. He hails from the field of physics

One of Imre important findings was that with advanced

and astronomy, who later took a transition into the IT and

analytics, he asserts that one can always find a way to

business world. In the first half of his career, he was a data

deliver the real value. Imre has been working in the

scientist who developed several machine learning

financial, telecommunication, and digital business

models in the fields of financial risk management,

sectors in various roles like a customer, consultant, or a

customer churn, fraud detection, and analytical CRM. In

vendor. Earlier, he used to work with large enterprises

the other half, he worked in more strategic positions,

and start-ups, and he now owns his own start-up. All this

such as a data science leader. 4

proves that data science can have a huge impact on businesses.

51

The digital marketing is getting more and more complex where AI services are collaborating and competing under the surface. In such a complex environment we all need AI to be competitive. Fight AI with AI!

Imre Szücs CEO/Co-Founder optimAIze Marketing Solutions www.optimaize-marketing.com

52

He also adds that to become a successful data science

itself. And none of them are less important than the

leader, the most important factor is to understand how

other.

one can support the short and long-term strategy with

The data science field is developing extremely fast.

data science clearly. Imre focuses on 3 pillars: firstly,

Besides the traditional relational data, now text, voice,

living together with the company by working on the daily

picture, video, genomic and sensor data, and a lot more

challenges. Secondly, setting up the capabilities to

can be processed even in the cloud, on high-performance

support future growth. And finally, driving innovation

computers, or edge devices and Imre as an experienced

and starting to work on new revenue streams.

data science leader comments that the data science leaders, must be able to make decisions or

Vanquishing all the Challenges with Confidence

recommendations on the usability of all these new

Imre mentions, "As I was really in love with AI and

capabilities. Putting in a nutshell, he also adds that the

statistics, it was a kind of personal challenge to see that

eager to learn attitude is vital.

my colleagues must be more experienced in certain

“But even with the best data science tools, leaders

topics than me." At the beginning of his leadership

cannot provide useful results without understanding the

journey, he learned to maximize the team's performance

business challenges. Open and communicative leaders

and be more of a leader than a specialist.

are more likely to succeed. And to make the data and

Everywhere he went, he not only learnt new data

analytics strategy in line with the business strategy, a

science techniques but he also made sure to learn the

strategic mindset is also necessary” Imre mentions.

new domains within it. Imre stresses that the importance of domain knowledge should not be undermined, instead

Boosting Marketing ROI Using Disruptive Technologies

one must find a way to channel it into their activities. He

Based on his experience, Imre says that the companies

states that he is still learning a lot by participating in

can improve efficiency or increase the ROI by 10%-40%

computational genetics and IoT analytics projects where

with AI compared to the traditional methods. At the

it's necessary to be a bit of a biologist. Fortunately, he is

same time, he also contradicts the statement claiming it

always enjoying learning new domains.

to be just the beginning and that machine learning, along with big data can give the industry much more in fields

Attributes that Make Up a Leader Data Science

like genetics, material sciences, computer vision, and

Imre asserts that one of the main challenges in his career

many more. The possibility to use cloud-native services

path was learning to balance between the strategy, fast-

can radically decrease the time-to-market.

developing data science technologies, and the business

53

A data science leader must be able to get the broader

opines that the digital world brings us tremendous

picture, involve all the necessary experts to behave in an

advantages, but also new risk elements, and a new way of

ethical and legally correct way.

operation.

OptimAIze mainly focuses on increasing the marketing

“To be effective in this modern world sophisticated

ROI by using the machine learning-based optimization

services are needed, and there is not enough time and

service firstly on Google Ad spend. The company is in a

resources to develop everything from scratch. In digital

fortunate situation as the team could experience the

marketing the noise is already huge, to target the right

detailed customer needs directly in the projects and then

audience is far more complicated than a few years ago.

targeting exactly those needs. But as the digital world is

Nowadays a huge ratio of marketing spending is useless.

changing rapidly, and the competitors are developing

He also believes that with the company's proven, AI-

extremely fast, the team also has to monitor the market

based cloud services, optimAIze can be a trusted partner

continuously and collect feedback from potential

in digital operation to increase ROI” Imre adds.

customers. Flexibility, scalability, and customercentricity on the other side are the main driving forces on

A Piece of Advice for Budding Data Professionals

the technological side along with disruptive technologies

Lastly, he has a strong message for aspiring data

to boost ROI.

professionals. Imre says, "Depending on where are you coming from, keep in mind that data science is in between

A Future Filled with AI Expansion

the statistics, technology, and business. Be open to

As the usability of AI is expanding, the market now has a

continuous learning and development and build a team

better understanding of where to use cloud services, and

that can put all the necessary capabilities together." He

it's only increasing.

advises that data science must be strongly connected to

Imre envisions that digital transformation has started or

the business to successfully help it.

almost had already finished in all the industries. He

54

FARZANA NOORZAY

Head of the Data Science, Analytics, Engineering and Data Products teams

Tillster, Inc. 56

Empowering Innovative Solutions with Data Science and Analytics

T

illster, Inc. is a global leader in powering digital

analytics, engineering and data products teams at

ordering, delivery, couponing and loyalty

Tillster. She has become the best leader because she had

solutions, across web, app, kiosk, and call center

very similar roles in the last two positions. She is

platforms. The company empowers restaurant brands

responsible for the data products, including actionable

that are looking to increase their revenue, achieve

dashboards used by decision-makers to define the

operational efficiencies, and to better engage the guests.

course of action for promotions based on the consumer

Enabling nearly 50 million digital orders per year, Tillster

behavior trends. Tillster also leverages AI products to

offers the added dimension of integrated marketing and

help understand consumers and provide appropriate

data mining, including a proprietary AI and machine

incentives, messaging and shopping cart

learning tool. Tillster serves more than 100 global and

recommendations. Of course, the backbone of these

regional brands, including Burger King, Baskin-Robbins,

products is the data infrastructure, data pipelines,

Freddy's Frozen Custard & Steakburgers, Godfather's

algorithms, models, and calculations. Farzana enjoys

Pizza, Jollibee, and Pollo Campero, with teams spread

being responsible for the products that affect clients as

across in North America, Latin America, EMEA, and

well as the restaurant consumers. Farzana's career has

APAC.

always revolved around data from when she was an intern at the County of Santa Barbra as a college student.

A Passionate Leader Driving Innovative Products

She built a payroll management system with MS Access

Farzana Noorzay is the Head of the data science,

2.0, where she found it very rewarding and even believed

57

It is essential to understand each aspect that a ects the products, from the infrastructure to the algorithms and models, the software development process, the users and personas, and the business case.

Farzana Noorzay Head of the Data Science, Analytics, Engineering and Data Products teams Tillster, Inc. www.tillster.com

58

it was fun to build something and see it in use. Farzana

services. She thinks that it has come downstream and

has worked in many industries such as entertainment,

become a vital part of almost every aspect of technology.

healthcare, fintech but always remained vigilant about

Consumers and users almost expect it. “This is an exciting

staying very close to data engineering.

and welcome change. Cloud computing has enabled big

Early in her career, Farzana was responsible for building

data and AI to that sounded like science fiction in the

data and server architecture. As a result, understanding

early 2000s,” adds Farzana.

the base level of data products was valuable in helping her lead and guide decisions now.

The Qualities of an Ideal Data Science Leader

“It is essential to understand each aspect that affects the

Over her career, Farzana has learned many valuable

products, from the infrastructure to the algorithms and

lessons within her field. In turn, she feels every data

models, the software development process, the users

science leader should be naturally curious with a

and personas, and the business case,” believes Farzana.

thoughtful approach to getting at the most critical metrics and indicators. They should ask provocative

Dedication at Every Single Step

questions that go against the grain instead of finding

Farzana spends time understanding the user, their

proof for their hypothesis. “It takes a certain level of

needs, limitations of the tech stack, the ecosystem, and

exposure to and expertise in an industry to be successful.

the industry. Then she tries to introduce relevant

It is advisable to spend time learning about the scenarios,

features that can solve a specific need or gap. She is very

risks, and drivers of the industry,” said Farzana. For

collaborative, so she discusses proposed solutions with

example, when she worked for a company where she led

an internal team from various levels in the organization

the data science products for large live venues. She spent

and then completes the market research to confirm that

her time learning about the operations, the business

there is a market need. However, most of the time is

partnerships, and the players of that industry. Farzana

consumed with resolving a tech debt or meeting some

had never thought about what it took to run a stadium

baseline needs while developing solutions.

that could host 20,000 guests for a four-hour game until that point. This taught her that the technical approach

Overcoming the Challenges with Disruption

for data science must be sensitive to the real-world

When Farzana worked for health insurance companies in

scenario. And thereby, stresses that emerging data

the early 2000s, AI had a very different term (actuary)

science leaders consider the differences between the

and was left to the statisticians who analyzed risk. Today

data science needs of a stadium and the data science

AI is integrated into many parts of Tillster products and

needs for building wealth management tools or

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healthcare messaging. “The data science leader should

scratch. “People tend to enjoy putting things together

possess adaptability and curiosity,” concludes Farzana.

and building from the ground up, but sometimes it's more strategic to use work that someone else has done in the

Experiences that Shaped the Leader

area and take it further or integrate it into the

Early in her career, Farzana found it hard to take on

workstream,” she adds.

leadership, authority, and decision-making. She shied away from roles that required more authority because

A Road Ahead with a Targeted Approach

she needed to hone her skills before feeling more

Farzana believes Tillster is on a clear path to rise to the

comfortable in setting a direction. After several years

top of the industry and be the catalyst for a smarter

when she served in individual contributor roles, Farzana

technical enablement of the food and beverage industry.

found herself stuck. “It was as if people perceived me as

In terms of data, it has a wonderfully strategic data

someone who could not lead,” she explained. Farzana

culture that starts from the top, and the CEO drives this

regretted her earlier decision not to accept her

culture herself. Her leader, Hope Neiman, the CMO, does

leadership roles. Eventually, after years of dedication

a fantastic job by setting a culture of producing high-

and experience, she was able to get her career moving

quality market-relevant data products. Farzana

towards higher levels of management, and she is grateful

regularly performs a deep-dive analysis of the data, and

for that.

with leadership like this, Farzana and the team are inspired to help fuel their vision. It provides a targeted

Words of Wisdom for Emerging Leaders

approach to outcomes-focused menu building, food

Farzana has two pieces of advice for the emerging

ordering and operational efficiencies, and incentives and

leaders--- first, she insists everyone keep learning and

loyalty programs that can be mapped directly to revenue

stay dedicated to personal growth and skill development

and profits. The industry has been trending toward faster

at every stage in their career. Second, she asserts that it is

ordering through various channels, with in-store kiosks

critical evaluate the buy versus build notion. In some

becoming the most sought-after ordering channel in Q3

cases, it may be best to buy or partner with an

of 2021 across the globe.

organization compared to building a product from

60

RAVI

KORLIMARLA

Director of Data Science and Advanced Analytics

Workday

62

Driving Innovation in Enterprise Software with Cutting-Edge Technologies

W

orkday is a leading provider of enterprise

Past Experiences that Helped in Learning

cloud applications for finance, HR, and

There are a few lessons that Ravi learnt from his past

planning. The company was founded in

experiences that he still follows even today. First and

2005 and it focuses on delivering solutions related to

foremost, of them is to prepare the business and an

financial management, human capital management, and

ecosystem to catch advanced analytical outputs. “It is

analytics applications designed for the world's largest

very important to educate leaders and to increase

companies, educational institutions, and government

awareness around the possibilities of utilizing AI and ML

agencies. Several organizations ranging from medium-

models in the workflows” he adds. Second of them is

sized businesses to Fortune 50 enterprises have selected

that—to learn to never overestimate the impact of an AI

Workday to address their needs for their promising

and ML projects. He also stresses that benefits come in

solutions.

slowly at first but compound over some time and hence it is always important to start slow, prove the benefits on a

A Passionate Leader

small canvas and expand from there.

Ravi Korlimarla is the Director of enterprise data science and advanced analytics at Workday. He has been

Turning Challenges into Opportunities

interacting with data teams and leading data projects

It has been some time since Ravi took a transition from

over the past 15 years, which has imbibed a certain

data and business SME to a data science leader and his

interest and passion for data and analytics in him. Ravi

journey was not at all smooth. He says that a part of his

hails from a computer engineering and ERP background.

challenge was to find opportunities to apply his newly

Therefore, he has an ample exposure to these systems,

acquired data science chops in a real business scenario,

and processes that create these data structures.

and he did not get the luxury of doing that, every time.

According to him, the next natural progression in the AI,

Initially, whatever little opportunities met his way, he

ML, and big data space would be to apply algorithms to

seized them all to deploy his models and this way Ravi

predict, diagnose, and forecast..

was able to generate a business impact.

Most of his models were path-breaking and first-of-its63

In the near future, while AI and ML may be used to validate the God Equation to benefit science, the most widely anticipated and applauded application of data science will be around e cient allocation of water and food resources to reduce hunger and water-borne diseases .

Ravi Korlimarla Director of Data Science and Advanced Analytics Workday www.workday.com 64

kind in the organization, however, his transition was long

jumping into this space, with a sense of being left behind,

and needlessly painful. But Ravi never looked back and

often with no clear vision, commitment, and strategy.

always made a point to turn in challenges into opportunities to learn, seek and explore.

An effective growth model like propensity to buy alone has the potential of adding 5 to 8% to the company's top

Attributes that Make a Successful Data Science Leader

line. Other such models can add millions back to the

Ravi mentions that leading data science teams is exciting

company's bottom line. In his opinion, the key for future

and challenging at the same time for him. “Recently, there

leaders is to establish the vision and gain commitment

has been a growing interest in this field, so hiring talent is

from the rank and file of the company. This needs to be

not a problem but retaining the talent is” he stresses. Ravi

followed with a well-thought-out strategy that

expresses that a good data science leader recognizes this

articulates a well-orchestrated and connected AI and ML

fact and fosters a creative, fun, and friendly environment

model blueprint for the company which has the potential

with care. An efficient leader leads by example, and this

of giving back millions to the business while staying

applies to data science leaders as well. Being the

ahead of the competition.

lighthouse of thought leadership of new approaches to model building and adopting best practices in

A Future Filled with Opportunities

implementing a frictionless MLOps process while

Ravi states that leveraging AI/ML to drive growth,

consistently positioning the team to deliver high

reduce OpEx, drive employee engagement, and to

business impact, inspires and motivates the team.

optimize community investments will create some

"I have used this approach, and as a result, I have

opportunity areas for the industry. Some companies are

consistently maintained teams with attrition rates, well

already leading in some of these areas, while others are

below industry averages" he concludes.

actively investing. "It is going to be an exciting journey for the industry as they embark on some of these ambitious

Embedding Innovation to Leverage Success

programs for the betterment of the business top and

Ravi believes in innovation that arises organically from

bottom lines and for the benefit of the world community”

the thought process that is directed towards solving a

states Ravi.

problem. He intends to deeply examine the current state of the business, multi-dimensionally, focusing on people,

Words of Enlightenment to the Aspiring Leaders

process, technology, and data. Typically, gaps can be

Enlightening the aspiring leaders Ravi says that big data,

multi-fold with many facets to be optimized. An

AI, and machine learning spaces are expanding faster

innovative solution addresses most of these gaps with

than the universe, so the space reveals blind spots that

the least disruption and effort and AI and ML solutions

have the potential to both warp or expand, one's

wonderfully suit such situations and hence he believes

knowledge and intuition. “Extreme caution needs to be

that the best disruption always changes the underlying

exercised in adopting open-source concepts. It is

paradigm, with the target audience hardly noticing the

important to learn, but given the maddening speed of

change at all.

advancements, it is also important to possess accurate wisdom and apply the newly acquired knowledge multi-

Disruptive Technologies that Drive an Impact

dimensionally” he claims. He goes on to say that it wi to to

Ravi asserts that big data, AI, and ML solutions can be

stay away from buzz words and fancy concepts and stick

hugely disruptive if they are executed with a vision and

to the elements that will benefit the leader, the team, the

targeted for an impact. Companies today are hastily

company, and the community.

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