Demonstrate business impact from a project
Overview
This question evaluates a data scientist's ability to quantify and communicate business impact, testing competencies in problem framing, metric definition, causal measurement (experimentation or quasi‑experiments), uncertainty quantification, and stakeholder leadership.
Uber
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Behavioral & Leadership
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Behavioral Deep-Dive: Business Impact of a Prior Project (Technical Screen)
Context: In a Data Scientist technical screen for a large two‑sided marketplace, you will be asked to deep‑dive one prior project with quantifiable business impact. The interviewer expects clarity on problem framing, decision‑making, measurement, and stakeholder leadership.
Instructions: Select one project and cover the following in order.
- Problem and Decision
- Define the business problem and why it mattered.
- State the specific decision your work enabled (e.g., launch, iterate, pause).
- Metrics
- Name the primary metric(s) and define them precisely.
- List key guardrails/secondary metrics.
- Counterfactual and Dollar Impact
- Baseline level, observed lift/change, sample sizes, time window.
- Counterfactual logic (what would have happened otherwise).
- Quantify dollar impact; include confidence/uncertainty.
- Measurement Strategy
- Experiment vs. quasi‑experiment and why.
- Key assumptions (e.g., SUTVA/no interference, parallel trends) and how you validated them.
- Any variance‑reduction or clustering you used.
- Execution and Adoption
- Stakeholder pushback/misalignment and how you handled it.
- Risks/guardrails, kill‑switches, and how you monitored them.
- How you secured adoption and de‑risked rollout.
- Delivery Details
- Timeline by phase; major trade‑offs made.
- What failed or surprised you; what you’d do differently next time.
Solution
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