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  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.