Sailthru/Articles (publication unknown) · 2015-03-25 · 4088d

Building Exceptional Data Science Teams: A Systematic Hiring Framework

Jeremy Stanley, Chief Data Scientist at Sailthru, presents a data-driven hiring methodology for recruiting remarkable data scientists that improves accuracy, reduces candidate loss, increases offer acceptance, and minimizes hiring effort simultaneously. The approach emphasizes designing interview processes that mirror actual job requirements rather than following traditional hiring practices. Stanley shares principles and implementation strategies influenced by leaders like Riley Newman at Airbnb and Drew Conway.

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Metrics in this report

Candidate Offer Rate Target

80%

goal

percentage of great candidates who should receive offers

Competing Offers Per Candidate

3count

minimum

average number of competing offers for strong data science candidates

Hiring Accuracy Target

90%

goal

percentage of hires becoming exceptional employees

Hiring Time Allocation Target

10%

maximum

percentage of team time spent on hiring activities

Offer Acceptance Rate Target

65%

goal

percentage of extended offers that should be accepted

Traditional Hiring Accuracy

50%

median

current state accuracy for most managers

Traditional Hiring Time Allocation

20%

minimum

current state time spent by hiring team

Traditional Offer Acceptance Rate

50%

maximum

current state success rate in competitive data science market