Behavioral + Technical: End‑to‑End Data Story (Ambiguous Problem)

You are interviewing for a Data Scientist role. Describe an end‑to‑end instance where you used data to solve a poorly defined business problem.

Cover the following explicitly:

1. Problem and hypothesis

• What was ambiguous about the problem and what decision needed to be made?
• State a precise, testable hypothesis.

2. Data and known biases

• List each data source you used and what it measured.
• Call out known biases or limitations (e.g., selection bias, missingness, seasonality, measurement error).

3. Method and validation

• Your statistical method/model and why you selected it over alternatives.
• How you validated assumptions (e.g., randomization checks, parallel trends, overlap, collinearity, calibration).

4. Causal identification and counterfactual

• The counterfactual strategy you used (e.g., randomized holdout, difference‑in‑differences, propensity weighting).
• Show how you avoided a naive before–after comparison.

5. Data quality and governance

• Specific data quality issues you encountered and how you remediated them.

6. Results with uncertainty

• Quantified impact with effect sizes and defensible uncertainty (confidence intervals, p‑values, or Bayesian posterior intervals).
• Include the counterfactual estimation (e.g., difference‑in‑differences).

7. Communication and decision

• How you communicated trade‑offs and uncertainty to non‑technical stakeholders and informed a decision.

8. Next step with +10% data

• One concrete improvement you would make if you had 10% more data.