Originally presented at Hirepalooza by Katie Kent, Director of Outcomes at Galvanize. Learn how to apply growth hacking techniques to hiring data scientists including: -The right time to hire a data scientist -How to interview them reliably -Specific recommendations about where to find great ones

1. How to Hire Data Scientists
Hirepalooza June 2, 2015

2. Director of Partnerships
Zipfian Academy
Katie Kent
@k80kent
Director, Educational Outcomes
Galvanize

4. A Perfect Storm of Supply and Demand

5. Storage is cheap. And so much cheaper than it used
to be.

6. Technology
Year Capacity (GB) Cost per GB (USD)
1992 0.08 $3,827.20
1997 2.1 $157.00
2002 80 $3.74
2007 750 $0.35
2012 3,000 $0.05
Source: http://www.jcmit.com/diskprice.htm

7. There is more and more data all the time.

8. Unprecedented Data Growth

9. Data = business value.

11. Incredible demand for data scientists
stems from a perfect storm of cheap
storage, data growth, and recognition
that data = business value.

12. The Demand Problem
Source: LinkedIn Analytics

13. Traditional educational institutions are
not set up to produce industry-ready
data scientists.
The Supply Problem

15. ‘I look for 3 things in data scientists:
math, code, and business
understanding.’
- Hernan Asorey, VP of Product Intelligence at Salesforce

16. Data Science Unicorn

17. Math &
Stats
Computer
Science
Domain
Expertise
Machine
Learning
Software
Engineering Research
Unicorn
Data Science

18. It’s hard to hire a data scientist.

19. 3 Lessons from unicorn
hunting in the wild.

20. Before you start, know what
species of data scientist you
need.
1

21. “Data scientist” means different
things to different people.
Example: A Data Scientist at Facebook is a different
species compared to a Data Scientist at Netflix.

22. Software
Engineering
Data EngineeringMachine
Learning
Feature
Development
AnalysisTransmission
Visualization &
Communication
Project
Scoping
Product
Analytics
The Data Science Skills Map

23. The skills required for a specific role
depend on the ultimate deliverables.

24. Software
Engineering
Data EngineeringMachine
Learning
Feature
Development
AnalysisTransmission
Visualization &
Communication
Project
Scoping
Deliver business
insights.
Deliver ML
prototypes.
Deliver Data
Products.
Different skills = different deliverables.
Product
Analytics

25. Know what species of data scientist
you need before you go hunting.

26. Know How to Snag Them
2

27. Great data scientists often have 3
simultaneous offers, so salesmanship
is important.

28. Great data scientists want 3 things:
interesting data, a strong team, and
the opportunity to make an impact.

29. Large data sets
What (Great) Data Scientists Want
Interesting Data Strong Team
Different types of data
Opportunity to
Make an Impact
Accessible data Mentorship
Collaborators
Opportunity to learn Data is a priority.
Greenfield
opportunities.
Creation over
optimization.

30. Sell what you have. Be smart about
what you don’t.

31. Auxiliary sources
Maybe your data doesn’t sound like a selling point.
Small Data
Inaccessible data
Fix infrastructure
first.
>
>
CHALLENGE SOLUTION

32. Training budget
Maybe your team doesn’t sound like a selling point.
No mentorship.
No daily
collaborators.
Designate partners.
>
>
CHALLENGE SOLUTION

33. Seek an evangelist.
Maybe opportunity for impact isn’t a selling point.
Data not a priority.
Low-hanging fruit
are picked.
Seek a researcher
to push boundaries.
>
>
CHALLENGE SOLUTION
Optimization is a
priority.
Seek an operations
researcher.>

34. Get In Front of Them
3

35. Savvy data science teams share
content about their work in places
where data scientists hang out.

36. This creates an engine to drive
inbound applications.

37. ‘I get about 300 applications for every
posting.’
- Todd Holloway, Data Science Lead at Trulia for 4 years
who runs the SF Data Mining Meetup

38. Where (Great) Data Scientists Hang Out
Airbnb Nerds
Tech Blogs
Netflix
StitchFix
Technology
Tech Talks
Conferences
Data Science
Meetups
Channels
Twitter
DataTau
Competitions
Public Challenges

39. Most Active Data Science Meetups
SF Data Mining SF Data Science
SF Machine
Learning
SF DataKind
http://www.quora.com/What-are-the-best-data-
science-big-data-meetups-in-the-San-
Francisco-Bay-Area
Links and
summaries!
Data Science for
Sustainability
Bay Area Women
in ML & DS

40. Interesting Data Science Conferences
O’Reilly Strata MLConf PyData
DataWeek PAW
The Data Science
Summit

41. Great Examples of Tech Blogs
Airbnb Nerds
Stitchfix
Technology
Netflix Technology
•nerds.airbnb.com/data/
•http://technology.stitchfix.com/blog/
2015/03/31/advice-for-data-scientists/
•http://techblog.netflix.com/

42. Public Open Data Challenges

43. Data Science Twitter Celebrities
•@hmason - Formerly Accel
•@mrogati - VP of Data, Jawbone
•@djpatil - Chief Data Scientist, The White House
•@peteskomoroch, Formerly Linkedin
•@josh_wills, Dir. Data Science, Cloudera
•@johnmyleswhite, Co-author of Machine Learning for
Hackers
•@hackingdata, Formerly Chief Scientist at Cloudera
•@bigdata, Chief Data Scientist at O’Reilly Media
giladlotan.com/2012/11/mapping-twitters-python-
data-science-communities/

44. datatau.com: The Hacker News of Data Science

45. Know How to Pick Them
4
*Bonus Lesson!*

46. The New Data Science Interview Process
Onsite Interviews
Check for a Pulse
Offer
Take Home Assignment
Less bias, less time, more accurate.

47. If you interview data scientists, go
read this article:
firstround.com/review/how-to-consistently-hire-
remarkable-data-scientists/

48. Galvanize Can Help
5
*Double Bonus
Lesson!*

49. Galvanize trains data scientists
through immersive educational
programs.
12-week Immersive
(fka Zipfian Academy)
GalvanizeU Masters’
Program

50. Alumni Employers

51. https://dshiringdaysf.eventbrite.com
Hire a Data Scientist from Galvanize

52. THANK YOU
KATIE KENT| DIRECTOR, EDUCATIONAL OUTCOMES
@K80KENT
www.galvanize.com