Managing Teams for Data Science, Analytics, and AI (CXOTalk # 326)


For senior leaders, managing data science is crucial to gain the most benefit from investments in AI and data analytics. Effective management involves people, culture, technology, and process. In this episode of CXOTalk, industry analyst, Michael Krigsman, interviews a top data scientist and business leader, who shares advice on how to create and manage a data science team.

To read the complete transcript, see:

Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years he has led data driven efforts in R&D and built multifaceted strategies for the industry. He has been a data science leader at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists. In his current role, his data driven strategies with machine learning, analytics, engineering, marketing, and behavioral psychology components had a disruptive impact on a multi-billion dollar industry sector. Bülent’s previous appearance on CXOTalk was popular and engaging.

Topics discussed:

Unique aspects of data science

From a management standpoint, what makes data science unique?
Is there a distinction between managing a data science vs. an AI organization?
Give us an overview of the unique challenges in managing data science?

ROI and organizational expectations

Who should own data science or AI in the organization?
Where should data science report?
Under Engineering, as a standalone organization, or someplace else?
To what extent is data science an ROI or R&D basic research function?
Is it imperative for corporate data science teams to maintain ongoing relationships with academics and external researchers?
What are reasonable ROI expectations from the data science group?

Talent profile

What characteristics make a great data scientist?
Do you need a technician, scientist, or business person?
When hiring, what skills should you prioritize?

Talent management

Are there unique points on managing a team of data scientists?
What about mentoring and ongoing training?
What drives employee retention for data scientists?
How can you make your workplace attractive to data scientists?
How can a company entice data scientists when they are so much in demand?



Comment List

    December 10, 2020

    it"s very informative.

    December 10, 2020

    Great interview that differentiates AI and data science projects from traditional IT projects.

    December 10, 2020

    Fascinating and illuminating interview! One of the key areas of Leading Digital.

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