Data science case interview practice
Work through one open data science case out loud: define success for a product, design an experiment or a measurement program, or estimate an effect you cannot randomize. Vera asks about your choices and changes one thing at a time.
- 45 to 50 min
- One, end to end
- Up to two small nudges
- Shared whiteboard Vera can read
How the round runs
- 01
Requirements and scope
Vera states the case in a sentence or two and invites questions. Pin down the decision it serves, the users, the scale and your assumptions before you name a metric.
- 02
High-level design
Walk through your approach end to end: the metric and its exact definition, the experiment or analysis, and the data it needs. Expect questions about the data model and storage too, because this round still uses the general design stages.
- 03
Deep dive
Vera picks the riskiest or least explained part, often the metric definition, the unit you randomize or how a long-run effect is estimated, and asks how it actually works here.
- 04
Scale and failures
One twist at a time from the case, for example revenue up while cancellations double, a deadline shorter than the effect you need to measure, or new signups that look like bots.
- 05
Wrap-up
Name the weakest parts of your plan, how you would watch the result after launch, and what you would do next.
What the report scores
Each skill gets a score from 1 to 5 against the bar for your level, with the quotes it is based on and the time you said them. The report ends with one thing to fix next.
- Requirements and scoping
- Structure and driving
- API and data model
- Architecture
- Depth
- Trade-offs
- Scale and failure handling
- Communication
At lead level, also: Operability and cost, Scope and influence.
How the real round works
The data science case round, also called the product case or metrics round, usually runs about 45 minutes around one open product question: define success for a feature, design an A/B test, or find out why a metric moved. The guides we read agree on the order that works: the decision the analysis serves, one precise primary metric with guardrails, the experiment or analysis design, then a recommendation. The mistake they report most is reaching for a test statistic before the metric and the design are settled.
How data science case interviews work: structure, signals and common mistakesRegular and lead level
- Regular
- Vera guides gently. Stall and you get a small prompt about the next part of the case; skip the scope and you are brought back to it.
- Lead
- Vera does not structure it for you. You drive the case, question the product hypothesis and cover cost, operations and team impact.
Length: 45 minutes at regular, 50 at lead.
See the feedback before you start
Full reports from the real report pipeline, for a fictional student and a scripted interview.
Questions
Is it scored like a data science case?
Not yet. Today the data science case uses the same five stages and the same skills as every system design round, so the report also scores the API and data model and the architecture. The cases, their clarifications, deep dives and twists are data science ones.
What kind of cases come up?
Metric frameworks for a product or a marketplace, experiment design including price tests, causal questions where you cannot randomize, trust in a dashboard and data quality, analytics pipelines and forecasting.
Do I have to compute statistics?
Mostly not. The case is about the design: the metric, the unit, how long to run and what could bias the result. Statistics on their own, such as power and p-values, are in the statistics and experimentation round.
Can I draw?
Yes. A shared whiteboard Vera can read is there for a metric tree, a funnel or the experiment plan.
Is the sample report a data science case?
The linked sample is a general system design round. It uses the same stages and scorecard the data science case uses today, so it shows what your report will look like.