Talent Connect October 2015 Competing with Talent Analytics How to build a talent analytics function
Jan 23, 2017
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title title title title title title title
Talent Connect October 2015
Competing with Talent Analytics How to build a talent analytics function
Expectations for talent analytics
How to build a talent analytics function
Focus on business problems, not data
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
How do we acquire the technical talent to meet our growth objectives?
©2015 LinkedIn Corporation. All Rights Reserved.
How many engineering recruiters do we need? Forecasted hiring needs # of Hires
Headcount forecasts # of FTE
2015 2016 2017 2015 2016 2017
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
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
What are the most attractive regions to hire SW engineers?
Supply of software engineers in region
Dem
and
for
softw
are
engi
neer
s
Findings: Labor Insights What 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
Findings: Labor Insights Used 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
Trai
n P
redi
ct
Findings: Labor Insights Where do we find critical skills? Engineering track concentration by region
Below average Above average
Systems & Infra Apps Data Mobile
Eng Manager
Eng Services OpsIT
What we have learned and where do we go next?
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
Where do we go from here?
§ Diversity & inclusion
§ Workforce strategy
§ Leadership
§ Quality of hire
§ Talent metrics and business outcomes
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
©2015 LinkedIn Corporation. All Rights Reserved.