Clustering Data Science Interviews Seven Related but Distinct Categories
summary
Claims seven distinct interview types based on more than 30 phone screens and a short taxonomy that maps roles to the data stack: analyst (SQL, Excel), alchemist (algorithms, optimization), data janitor (pipelines, AWS/Redshift) and others. Notes common mismatches — over‑specifying Scala/Spark or pop‑quiz stats — and practical tools: take‑home challenges, code reading/debugging, portfolio review, live coding or phone screens. Reports a surprising split: roughly 75% data cleaning vs 25% modeling, and recommends a sensible cadence: HR screen, take‑home, then onsite discussion.
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chapters (6)
- 0:00Data science interview challenges
- 3:15Data science roles spectrum
- 11:03Hiring process pitfalls
- 16:26Evaluating candidates
- 23:53Realistic interview tasks
- 28:13Q&A
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