How to hire and test for data skills: A one-size-fits-all interview kit

Given the recent demand for data analytics and data science skills, adequately testing and qualifying candidates can be a daunting task. Interviewing hundreds of individuals of varying experience and skill levels requires a standardized approach. Tanya Cashorali explores strategies, best practices, and deceptively simple interviewing techniques for data analytics and data science candidates.

Most people agree that interviewing is one of the most difficult and least enjoyable professional activities. Given the recent demand for data analytics and data science skills, it has become an increasingly daunting task for managers to adequately test and qualify candidates. Interviewing hundreds of individuals of varying backgrounds requires a more efficient way of quantifying technical and cultural fit. This demand led to the creation of a deceptively simple data exercise, which reveals a surprising amount of information about interviewees. This test has been administered to dozens of candidates of varying experience levels and formal backgrounds. Data science is a highly integrated discipline. The variance in solutions provided by a physicist compared to a computer scientist is fascinating. Tanya Cashorali digs deeper into these approaches and provides recommendations on how to administer and review test results for each type of candidate. You’ll receive a link to the publicly available dataset, the test questions, and the scoring rubric and learn how to save time vetting candidates, move away from unrealistic whiteboarding interviews, and start hiring data scientists who will quickly provide business value.