The document discusses building a talent analytics function focused on addressing business problems and achieving quick wins to enhance credibility. It highlights the need for effective forecasting and recruitment strategies, particularly for engineering talent, while analyzing regional supply and demand for software engineers. It concludes with insights on future directions in diversity, inclusion, and the importance of aligning talent metrics with business outcomes.

1. ​ Talent Connect
​ October 2015
Competing with Talent Analytics
How to build a talent analytics function

3. Expectations for talent analytics

6. How to build a talent analytics
function

7. Focus on business problems, not data

8. Leap-frog strategy
What we had Business
demand
Our solution
Analytics Infrastructure Reporting
Team resource allocation
§ Building the IT
infrastructure is a long
journey…
§ Reporting will consume
100% of capacity and
never be 100% accurate
§ Prioritize quick wins that
solve business problems to
build credibility

9. How do we acquire the
technical talent to meet our
growth objectives?
©2015 LinkedIn Corporation. All Rights Reserved.

10. How many engineering recruiters do we need?
Forecasted hiring needs
# of Hires
Headcount forecasts
# of FTE
2015 2016 2017 2015 2016 2017

11. Are we hiring the right mix of people?
Org. shape has shifted over time
% of Engineering FTE
2013 2014
2013 2014
Senior+
Mid-Level
Entry-Level
Hiring has focused on entry level…
% of new hires

12. Partnered with HRBP and talent acquisition leads to
double mid-level and senior hires
# of new hires
1H 2014 1H 2015
Senior+
Mid-Level
Entry-Level

13. What are the most attractive
regions to hire SW engineers?

14. Supply of software engineers in region
Demandforsoftwareengineers
Findings: Labor InsightsWhat is the supply and demand for SW engineers?
Seattle
Chicago
Boston
Washington D.C.
New York
SF Bay
Houston
Denver
Philadelphia
Atlanta
Dallas
Toronto
LA
Raleigh-Durham
Montreal
Austin
San Diego
Detroit
Minneapolis
Phoenix
High
Low
Low High

15. Findings: Labor InsightsUsed profile data to classify SW engineers into tracks
*18 most common skills among LinkedIn’s current engineering HC: Java, Python, Linux, Distributed Systems, C++, JavaScript, Hadoop,
Scalability, C, Algorithms, Perl, Software Engineering, Git, Unix, Software Development, REST, Agile Methodologies, Ruby
LI Profile features
LI Profile
Features
Candidates from ATS
Machine learning
algorithm
Classification model Classified profiles
TrainPredict

16. Findings: Labor InsightsWhere do we find critical skills?
Engineering track concentration by region
Below average Above average
Systems &
Infra Apps Data Mobile
Eng
Manager
Eng
Services OpsIT

17. What we have learned and
where do we go next?

18. What we have learned in the past 18 months
§ Focus on solving business
problems with data
§ Prioritize quick wins to build
credibility
§ Partner to drive change and
business impact

19. Where do we go from here?
§ Diversity & inclusion
§ Workforce strategy
§ Leadership
§ Quality of hire
§ Talent metrics and business
outcomes

20. What can talent analytics do for you?
§ Think of one business question
for your talent analytics team to
solve… scope project for ~1-2
months
§ Make sure the team has capacity
to focus… the “cost of yes” is
asking the team to re-prioritize
existing commitments

21. ©2015 LinkedIn Corporation. All Rights Reserved.