Stripe·Data Scientist·Take-home Assignment·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Stripe gave me a 6-hour take-home for a Data Scientist role and the core challenge was basically a scoping exercise disguised as a modeling problem. The real test wasn't the prediction itself, it was showing you could manage time, communicate trade-offs, and not quietly drown trying to do 24 hours of work in 6.

Questions Asked (1)

You have 6 hours to predict a product's target users, but a thorough solution would realistically take 24+ hours. Write a one-page plan you'd send to the hiring manager before starting: define the problem and success criteria, prioritize what you'll actually deliver in 6 hours vs. defer, list the minimum data and clarifying questions you need upfront, call out risks like label ambiguity or class imbalance with mitigations, lay out your presentation storyboard, and explain how you'd defend scope decisions in Q&A if someone pushes you on skipping something like causal inference.

Adaptability & AmbiguityProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This one messed with my head a bit because my instinct was to just start building.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Frame the 6-hour constraint as an opportunity to demonstrate ruthless prioritization and stakeholder communication. Propose a minimal viable analysis that delivers actionable insights, explicitly stating what you will and won't do, and how you'll mitigate risks. Show that you understand the business context and can defend your scope decisions with clear trade-offs.

Pro tip: Tie every scope decision to business impact: explain how the 6-hour deliverable still de-risks the product decision, and offer a follow-up plan for the deferred 18 hours. This shows you're not just cutting corners but strategically sequencing work.

1. Define the problem and success criteria

Clarify the product's goal and what 'target users' means for this context. Propose measurable success criteria (e.g., precision@k, lift over baseline) that align with business objectives.

2. Prioritize deliverables for 6 hours

List what you will deliver: a quick data audit, a simple heuristic or baseline model, and a validation on a holdout set. Explicitly defer complex methods like causal inference, deep learning, or extensive feature engineering.

3. Identify minimum data and clarifying questions

Specify the data needed (e.g., user demographics, behavioral logs) and ask upfront questions about label definition, data freshness, and business constraints to avoid rework.

4. Anticipate risks and plan mitigations

Call out label ambiguity (e.g., define 'target user' precisely) and class imbalance (e.g., use stratified sampling, appropriate metrics). Propose quick checks and fallback strategies.

5. Storyboard and defend scope

Outline a one-page presentation: problem, approach, results, limitations, next steps. Prepare to justify skipped items (e.g., causal inference) by emphasizing time constraints and the value of a fast, iterative approach.

Key Points to Mention

Explicitly state what you will NOT do in 6 hours and why (e.g., causal inference requires more time and data).
Propose a simple baseline model (e.g., logistic regression or decision tree) and validate with a holdout set.
Ask clarifying questions about label definition, data availability, and business success metrics.
Address class imbalance with techniques like resampling or using precision-recall AUC instead of accuracy.
Present a clear storyboard: context, methodology, findings, limitations, and recommended next steps.
Defend scope decisions by linking to business impact and offering a phased plan for deeper analysis.

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