Data Science in HR

May 5, 2015
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(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Joseph Rickert

Last year in a post on interesting R topics presented at the JSM I described how data scientists in Google's human resources department were using R and predictive analytics to better understand the characteristics of its workforce.  Google may very well have done the pioneering work, but predictive analytics for HR applications is going mainstream. In the still below from a Predictive Analytics Times video on Data Science for Work Force Optimization Pasha Roberts, Chief Scientists at Talent Analytics, describes using survival analysis for modeling employee retention.   

Survival curve

The video begins with a discussion of data analytics in industry, spends some time on three important curves for workforce analysis, presents some tips for talent modeling and ends with a case study on call center attrition. During the course of his presentation Pasha walks through all of the stages of a project from formulating a hypothesis, through model building and testing to model deployment.

But Pasha covers more ground than model building alone. It appears that leading edge HR departments are moving towards predicting individual employee performance. The discussion of Aptitude Metrics about 45 minutes into the talk should be of interest to anyone working, or looking for work at a technology company. Quantitative evaluation is likely to be a big part of our future. This video is well worth watching. 

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