Data science mock interviews
Write SQL and pandas against real tables, reason through statistics and experiments out loud, and design a metric or an A/B test while Vera asks how you would check it.
- SQL and pandas, statistics, metrics design, behavioral
- SQL and pandas, run on the question’s data
- Regular and lead
The rounds
SQL & pandas
Write SQL and pandas to answer product questions from data.
45 min
Statistics & experimentation
Probability, hypothesis testing, A/B testing and modeling basics.
35 min
Metrics & experimentation design
Design an experiment, metric framework or analytics pipeline.
45 to 50 min
Behavioral
Stories about stakeholders, ambiguity, impact and influence.
30 to 35 min
A full loop for one role adds a recruiter screen (12 to 15 min) and a hiring manager conversation (25 to 30 min).
Worth knowing
Your query runs
A SQL run returns a result table. Vera judges the logic and the edge cases (ties, NULLs, empty input, duplicate rows from a join), not the dialect, and asks how you would check the result.
Regular and lead level
- Regular
- Correct queries and fundamentals, explained clearly, with sensible checks on the result.
- Lead
- You own the ambiguity: pick the metric, defend the experiment design and talk about impact and influence.
See the feedback before you start
Full reports from the real report pipeline, for a fictional student and a scripted interview.
Questions
Which SQL dialect is it?
A PostgreSQL-like dialect. Vera looks at the logic and the edge cases rather than dialect details.
What does the statistics round cover?
Probability, hypothesis testing, A/B testing and modeling basics, asked one question at a time with a probe or two for depth.
What is the metrics and experimentation design round?
The data science take on system design: design an experiment, a metric framework or an analytics pipeline, with the trade-offs explained.