1. Engineering Recruiting Today!
Daniel Portillo, VP of Talent, Greylock Partners!

2. Perspectives
Of an early-stage startup guy
LinkedIn User #47,201
Rypple Customer #1
Page 2!

3. Talent
shortage
Page 3!

4. Fine Print
Job postings and organic
website traffic is not
something I've focused on
over the years. Some
companies rely heavily on
them for candidates. I don’t
judge if that's how you do
things. I prefer going out
and getting who we want. I
believe it’s impossible to
build a great company
a n o t h e r o t h e r w a y.
Disclosure: We're investors in
LinkedIn & Treehouse.

5. TRENDS

6. spray & pray doesn’t work
trend is operational & data driven
to find the fish, be strategic & leverage the web
pool is small, but new ways of growing it
Page 6!

7. Recruiting is becoming
more operational…
Page 7!

8. …& data driven
Page 8!

9. 100% (X)
Total Out reach
(Top of funnel)
Avg Response Rate:
10% to 20%
10-20%
Basic
Metrics
0.7%
Intro to Offer Rate:
to
10-15:1
2%
0.3%
to
Acceptance Rate:
2%
40-100%
Page 9!

10. 1,450 Greylock Talent Team
! Outreach in Q1
320
(22%)! Response rate
Real !
Example
435! Total Introductions
29
Intro to Offers
(15:1)!
17
(59%)! Acceptance Rate
! Page 10!

11. Top of the funnel
Increase outreach
using github,
linkedin, etc.
Response
Rate
AB testing outreach
Levers
Intro to Debrief after
Offer interviews to improve
Rate
conversion
Accept
ance
Rate
Pre-closing
Page 11!

12. FINDING THE FISH

13. Don’t bring a knife to a gun-fight
Page 13!

14. Essential recruiting tools
Page 14!

15. To recruit well & fast,
match intent to relevant ability
Page 15!

16. How to (scalably)
match intent & relevance
Page 16!

17. Best sourcer: high relevant, high intent
“Pull Sourcing”
Scraping
Spamming
Relevancy
Agencies (do not scale well)
Job boards
“Push sourcing”
Intent
Page 17!

18. Matching algorithm
It s a data game
Tags (relevancy)
Behavior (intent)
Extracted tags from candidates &
Matching
Convert actions taken on Whitetruffle
companies profiles and other sources by companies & candidates to
available.
knowledge to improve matching.
Matching score
Human validation
Match is released
Page 18!

19. GIT WITH THE TIMES

20. Page 20!

21. Reading github profiles
can be tricky
Page 21!

22. Page 22!

23. Dive into the implicit Web
Page 23!

24. Explicit Professional Data
~500 chars
~1k chars
~10 chars
Page 24!

25. Implicit Data from the web
~250k chars
~80k chars
~100k chars
Page 25!

26. TalentBin’s 360 view
*
Extract professional activity
Analyze & surface skills
Aggregate all contact info
* he’s happy because he never updates LinkedIn, but now, with TalentBin, you can find him!
Page 26!

27. Page 27!

28. Get…
Page 28!

29. Job Posting 2.0
Page 29!

30. Grow
the pool
© Random House
Page 30!

31. Education is being
disrupted

32. Higher education is broken
with increasingly higher costs
for students and society at
larger. Education is no longer
a one-time event but a lifelong
experience. What is taught in
universities is a mismatch
with what employers need,
especially in technical fields.
Sebastian Thrun
Udacity Founder. Google Fellow. Stanford
University Comp. Sci. Research Professor.
Autonomous Car Inventor. 50 Smartest
People in Tech (Fortune). Education disruptor.
Page 32!

33. Page 33!

34. Big breakthroughs happen
w h e n w h a t ’s s u d d e n l y
p o s s i b l e m e e t s w h a t ’s
d e s p e r a t e l y n e c e s s a r y.
Higher education is
prohibitively expensive. But
web technologies now allow
100,000+ students to be
t a u g h t b y o n e S t a n f o rd
professor.
Daphne Koller
Coursera Co-Founder. 3rd generation Ph.D.
Stanford University Professor. MacArthur
Fellowship winner. Education disruptor.
Page 34!

35. Page 35!

36. Page 36!

37. To Recap…
trend is operational & data driven
There are new amazing tools, but there are no shortcuts
We can help create bigger and better pools of talent
Page 37!

38. Thank you
George!
Page 38!