This article outlines a structured framework for revamping data science interview processes to better assess candidate skills, reduce hiring errors, and adapt to evolving business needs.
•Common motivations for updating DS interviews include outdated content, question leaks, changing business needs (generalists vs. specialists), and the rise of generative AI reducing the value of syntax-heavy questions
•DS interviews are categorized as technical (SQL/Python pair programming, ML, statistics, EDA, data engineering) or behavioral (leadership, communication, strategic thinking)
•The revamp framework includes aligning on the problem, securing leadership buy-in, forming a working group, surveying the organization for gaps, and drafting a structured plan
•New interview questions should balance core skill evaluation, appropriate difficulty, clarity, uniqueness, and flexibility to assess senior candidates' deeper expertise
•Rollout involves 2-3 internal practice runs, live runs with real c
