We all know that the costs of hiring the wrong person can be high. Beyond incurring significant onboarding expenses, data science teams can face wide-ranging impacts—from project delays to increased attrition rates–when a new hire doesn’t work out. One data science leader we spoke with saw an 80% turnover rate on a team within six months of hiring a team manager. The new manager, it turned out, wasn’t open to feedback or willing to listen to the team’s ideas, resulting in the mass exodus.
Cautionary tales like this abound, leading organizations to create robust interview processes for the hiring of new data scientists. Such efforts often include some combination of technical interviews and perhaps coding assessments along with pointed behavioral questions to appraise a candidate’s “soft” skills–which are so vital to a well-functioning data science team and productive interactions with business staff.
But no process is perfect. There’s also substantial discussion these days on how to combat unconscious biases in hiring decisions given how easy it is for “intuition” to influence candidate selection. One prime example is when an interviewer evaluates one candidate’s ability to partner with business teams higher than others’, solely based on the person’s vibrant personality. Another is when interviewers assess a candidate as a great fit because they “connect” with the individual, sharing the same interests or alma mater.
Even beyond cognitive biases, data science leaders also need to worry about tackling what Wayfair, one of the world’s largest online companies for home goods, calls “noise.”
What exactly is “noise” in the interview process?
Patrick Phelps, who is Wayfair’s associate director of Data Science Measurement and Attribution will share what they mean by noise and how to deal with it in his presentation at Rev 2 in New York this week.
Some readers may remember Patrick from last year’s Rev summit when he participated in a panel discussion about ways to hire, retain and nurture data science talent. As part of the conversation, he shared his use of “learning interviews” to help assess whether candidates were willing to listen to and learn from company experts. (Check out the video to learn more about this interview method.)
In his session this year, you’ll learn:
This year’s conference, which will be this Thursday, May 23 through Friday, May 24 in New York, features more than 60 industry leaders sharing insights into the biggest challenges in data science today—from scaling models and building model-driven cultures to ensuring responsible AI, with sessions customized for both practitioners and leaders. You can see the full agenda here—which includes a keynote from Nobel Prize winner and noted behavioral economist Daniel Kahneman. Daniel will kick off the conference, discussing the key findings that have laid the foundation for modern behavioral economics and have illuminated how we really make choices.
If you haven’t signed up for Rev 2 yet, there’s still time.
Register here, and use code CB_Rev200 for a $200 discount.
See you there!