Google·Data Scientist·Take-home Assignment
May 2026
Got hit with a massive take-home style case at Google for a DS role. Two giant sub-problems back to back: a full end-to-end pricing A/B test design for a B2B SaaS company, and a 2x2 factorial experiment for a signup CTA. The depth expected here was genuinely surprising, way beyond what I'd seen in typical DS interviews.
- You join a B2B SaaS company with three pricing tiers. The PM wants to run a 2-week A/B test to raise list prices by 20% on the two lower tiers. Should you run a user-level A/B test or a quasi-experimental design? Lay out the pros and cons including contamination risk, fairness concerns, and operational overhead.
- Define the full success metric hierarchy for this pricing experiment. What is your primary metric, what are the guardrails, and what exact decision threshold would you use to call the test a win?
- Why is a 2-week runtime insufficient for this experiment, and what is the minimum viable schedule you would propose? Include your MDE assumptions, traffic constraints, unit of randomization, bucketing persistence strategy, and how you would handle sequential looks.
- Describe the full instrumentation and analysis plan: eligibility and exposure rules, identity resolution to prevent bucket switching, handling of discounts and coupons, seasonality controls, and how you would estimate LTV during the experiment while accounting for right-censoring.
- If the pricing test results are positive, what does your rollout and escalation plan look like, and how would you monitor for long-run effects or customer backlash after full launch?
- Design a 2x2 factorial experiment for a signup CTA with two factors: button color (red vs. blue) and position (top vs. bottom). Cover hypotheses for main and interaction effects, randomization, power and MDE per effect, multiple testing correction, downstream signup analysis, device stratification, and when you'd prefer chained tests over a full factorial.
“This is where I spent most of my time and still felt like I left things on the table.” The rest of the author's notes on Data Scientist interview at Google, Take-home Assignment round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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