Interview · Technical
Covers problem framing, experiment design, causal reasoning, model choice and evaluation, and explaining uncertainty. Suits mid to staff-level data scientists. Follow-ups push for assumptions, how results were checked and what happened once the work was used.
Suits: Data scientist, Applied scientist, Decision scientist, Product data scientist
Interviewer: Tell me about a time you changed someone's mind at work. How did you go about it?
Candidate: Our team wanted to rebuild the billing page from scratch, and I thought it was the wrong call, so we put together a plan to…
Interviewer: You said "we". What did you say yourself, and what finally changed their mind?
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Data · Technical
One booking drop, worked to a recommendation, showing how an analyst frames, slices and advises.
Listens for
7 stages · 40 min · Junior to Senior
Data · Technical
Finds out how a candidate turns messy questions and data into sound answers people act on.
Listens for
7 of 18 questions · 35 min · Junior to Senior
Data · Technical
Finds out how a candidate builds reliable data pipelines and models that others can trust.
Listens for
7 of 17 questions · 35 min · Mid to Staff
