Where should you put your data scientists?

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Daniel Tunkelang

I delivered this keynote at the Fast Forward Labs Data Leadership Conference on April 28, 2016. You can find related materials in the following publications: https://www.oreilly.com/ideas/where-should-you-put-your-data-scientists http://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/

Where should you put
your data scientists?
Daniel Tunkelang

Review: why you hire data scientists

To improve products

To improve decisions

Organizational structure matters

stand-alone
embedded
integrated

The stand-alone model

Pro: gives team autonomy

Pro: positions data as ﬁrst-class

Con: risks marginalizing team

Con: other teams like self-sufﬁciency

The embedded model

Pro: ensures utilization

Pro: ﬂexible allocation

Con: team resents lack of autonomy

Con: management challenges

The integrated model

Pro: organizational alignment

Pro: long-term investment

Con: dilution of data science

Con: lack of reuse

stand-alone: autonomy, data as ﬁrst class
embedded: utilization, ﬂexible allocation
integrated: alignment, long-term investment

stand-alone: marginalization, isolation
embedded: resentment, dual management
integrated: dilution, lack of reuse

You can mix and match

Adapt as company evolves

Remember the mission

Thank you.
Daniel Tunkelang
dtunkelang@gmail.com
: @dtunkelang

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Keynote at 2012 Semantic Technology and Business Conference Scale, Structure, and Semantics Daniel Tunkelang, LinkedIn Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000. Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data. In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.

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Where should you put your data scientists?

1. Where should you put your data scientists? Daniel Tunkelang

2. Review: why you hire data scientists

3. To improve products

4. To improve decisions

5. Organizational structure matters

6. stand-alone embedded integrated

7. The stand-alone model

8. Pro: gives team autonomy

9. Pro: positions data as ﬁrst-class

10. Con: risks marginalizing team

11. Con: other teams like self-sufﬁciency

12. The embedded model

13. Pro: ensures utilization

14. Pro: ﬂexible allocation

15. Con: team resents lack of autonomy

16. Con: management challenges

17. The integrated model

18. Pro: organizational alignment

19. Pro: long-term investment

20. Con: dilution of data science

21. Con: lack of reuse

22. stand-alone: autonomy, data as ﬁrst class embedded: utilization, ﬂexible allocation integrated: alignment, long-term investment

23. stand-alone: marginalization, isolation embedded: resentment, dual management integrated: dilution, lack of reuse

24. You can mix and match

25. Adapt as company evolves

26. Remember the mission

27.

28. Thank you. Daniel Tunkelang dtunkelang@gmail.com : @dtunkelang