Instacart·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Interviewed for a Data Scientist role at Instacart. The whole thing basically came down to one big case-style question that you had to walk through end to end, which sounds manageable until you realize how many layers they expect you to cover.

Questions Asked (1)

Walk me through a high-stakes business problem you solved using data. Cover the decision you were trying to inform, the hypotheses you formed, how you defined success, who the stakeholders were, what data you used, how you designed the analysis or experiment, how you handled confounders or missing data, how you validated the result, and what the measurable impact was. Also name one thing you'd do differently.

A/B Testing & ExperimentationProduct Analytics & MetricsStakeholder Management

Author's notes

This is basically a full case study crammed into one question and I underestimated how much they wanted on the validation piece specifically.

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AI HintsAI Generated

Suggested Approach

Choose a single high-stakes project where data directly informed a business decision, and structure your answer as a story that covers the decision, hypotheses, success metrics, stakeholders, data, analysis/experiment design, confounders/missing data, validation, and measurable impact. Emphasize the business context and your role in driving the decision, not just the technical details. End with a clear lesson learned and how you'd apply it next time.

Pro tip: Quantify the impact in business terms (e.g., revenue, conversion, retention) and explicitly connect your analysis to the decision made; interviewers at Instacart care about how data drives product and business outcomes.

1. Set the scene and decision

Briefly describe the business problem, why it was high-stakes, and the specific decision it needed to inform. Name the key stakeholders and their goals.

2. Hypotheses and success metrics

State the hypotheses you formed and how you defined success (e.g., primary metric, guardrail metrics). Explain how these tied to business objectives.

3. Data and analysis/experiment design

Describe the data sources, how you designed the analysis or experiment (e.g., A/B test, causal inference), and how you addressed confounders or missing data.

4. Validation and impact

Explain how you validated the results (e.g., robustness checks, sensitivity analysis) and the measurable impact on the business metric. Mention stakeholder buy-in and implementation.

5. Reflection and improvement

Share one thing you'd do differently and why, showing self-awareness and continuous learning.

Key Points to Mention

Clear articulation of the business decision and how data informed it
Well-defined hypotheses and success metrics aligned with business goals
Appropriate experimental or causal design (e.g., A/B test, quasi-experiment) with sample size/power considerations
Handling of confounders, missing data, or biases (e.g., via stratification, imputation, or sensitivity analysis)
Validation techniques (e.g., holdout, cross-validation, robustness checks) and stakeholder communication
Quantified business impact (e.g., revenue lift, conversion increase) and a concrete lesson learned

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.