Shortcuts that short-circuit talent pipelines: Data-driven optimization of hiring

Description

As acute shortages for data scientists and engineers further develop in competitive talent markets, it’s imperative to address biases and bottlenecks in the hiring of data scientists and engineers. Most companies approach hiring and talent management as an art, relying on judgment and experience when conceptualizing jobs, drafting JDs, and screening and assessing candidates.

Maryam Jahanshahi explains how often-innocuous recruiting decisions have dramatic impacts on hiring outcomes, from the success of a hiring process to the candidate pool composition and the amount of time it takes to hire. She then discusses how using data-driven approaches provide an arbitrage opportunity that has significant impact on both the quality and diversity of candidate pools for data science and engineering roles.

Maryam covers the role of confounders in the hiring process, explores the complexity of job descriptions, and shares how she extracted signal from noise to extract the key conclusions she presents. She then presents key hiring heuristics in three vignettes: the one word that creates significant ripple effects at every stage of the hiring process; the surprising way that startups do better in the talent war for data scientists and engineers; and hacking diversity, or how she and her team learned to reproducibly and robustly recruit a diverse pool of data scientists and engineers.