Lyft Data Scientist Interview: Questions & Prep (2026)

Lyft is one of the largest rideshare platforms in the US, and it runs a structured Data Scientist interview that candidates report as product-heavy and analytically rigorous. With 181 open Data Scientist roles listed as of July 2026, competition is real but so is the opportunity.

The process typically spans four to five stages: a recruiter screen, a take-home SQL or case assessment, one or two technical rounds covering statistics and machine learning, a product sense round, and a final behavioural round. Lyft interviewers typically look for candidates who can move between SQL, experimentation design, and business storytelling in the same conversation.

The roles skew toward product analytics and experimentation, with a strong emphasis on A/B testing, metric design, and causal inference. If you have built experience around two-sided marketplaces or rider and driver-facing features, that context lands well in the interview.

These are the questions candidates report most frequently across Lyft Data Scientist interviews in 2024-2026:

1. How would you design a north-star metric for rider satisfaction?
2. An A/B test shows conversion increased but revenue dropped. What do you investigate?
3. Walk me through how you would build a surge pricing model.
4. Write a SQL query to find the top 3 drivers by completed trips in each city over the last 30 days.
5. How would you detect fraudulent rides at scale?
6. A product manager wants to launch a new carpooling feature. What metrics would you track before and after launch?
7. Explain precision vs. recall. When would you prioritise one over the other?
8. How would you handle class imbalance in a churn prediction model?
9. A key metric dropped significantly overnight. Walk me through your investigation approach.
10. How would you measure whether a driver incentive programme is actually working?
11. Describe a time you disagreed with a stakeholder about what the data was saying.
12. How would you estimate how many additional rides Lyft could capture by entering a new city?

Questions 1, 2, 6, and 9 come up most often. Practise explaining your reasoning out loud, not just arriving at an answer.

Q: An A/B test shows conversion increased but revenue dropped. What do you investigate?

*Situation:* At my previous company, we ran a promotional discount experiment on a subscription product. Conversion went up noticeably, but weekly revenue was trending down.

*Task:* I needed to figure out whether the test was actually a win, a loss, or something in between before we recommended a rollout decision.

*Action:* I first checked that the metric definitions were consistent across both groups and that there was no data pipeline lag. Then I segmented revenue by user cohort: new users vs. returning. I found that the discount was converting a large volume of low-value first-time users who had a historically high churn rate, while our high-value returning users were barely affected. I also looked at average order value and lifetime value projections, not just immediate revenue.

*Result:* We recommended pausing the rollout and redesigning the offer for a specific new-user segment only, with a 30-day follow-up check on retention. The revised test showed a healthier revenue-per-user outcome.

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Q: Describe a time you disagreed with a stakeholder about what the data was saying.

*Situation:* A senior product manager was convinced that a feature we had shipped was driving a significant lift in engagement, based on a dashboard they had been watching.

*Task:* I suspected the lift was partly explained by a seasonal spike and a concurrent marketing campaign, not just the feature itself.

*Action:* I pulled a clean controlled comparison using a holdout group we had kept from the original launch. I also ran a difference-in-differences analysis controlling for seasonality. I presented both the original chart the PM had seen and my corrected view side by side, framing it as 'here is what the full picture looks like' rather than 'you are wrong.'

*Result:* The actual lift was closer to publicly reported industry norms for this type of feature. The PM appreciated the rigour and we agreed to run a proper A/B test for the next iteration.

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Q: How would you handle class imbalance in a churn prediction model?

*Situation:* I was building a churn model at a SaaS company where churned users made up a small fraction of the dataset, based on our internal sample.

*Task:* A naive model was predicting 'not churned' for almost everyone and still hitting high accuracy, which was useless for the business.

*Action:* I tried three approaches in parallel: oversampling the minority class with SMOTE, undersampling the majority class, and adjusting class weights in the model. I evaluated each using precision-recall AUC rather than raw accuracy, since accuracy was misleading here. I also tuned the decision threshold separately from the model training.

*Result:* The class-weight adjustment with threshold tuning gave the best business outcome: we caught a larger share of actual churners without flooding the customer success team with false positives.

For metric design questions: Start with the goal (what behaviour are we trying to change?), name a primary metric and one or two guardrail metrics, explain what a success threshold looks like, and flag any metrics that could be gamed.

For A/B testing questions: Cover experiment design (unit of randomisation, sample size, duration), then analysis (primary metric, confidence level, novelty effects, network effects if relevant), then decision criteria. Lyft interviewers typically want to hear about marketplace-specific concerns like driver-rider network spillover.

For SQL questions: Think out loud. State your approach before writing. Common patterns Lyft tests: window functions (RANK, ROW_NUMBER), aggregations with GROUP BY, date filtering, and joins across multiple tables. Write clean, readable SQL with aliases.

For product sense questions: Use a simple structure: understand the user, define success, propose metrics, anticipate trade-offs. Bring up both rider and driver perspectives since Lyft is a two-sided marketplace.

For machine learning questions: Cover the problem framing (classification vs. regression vs. ranking), feature engineering choices, model selection reasoning, evaluation metrics, and how you would monitor the model in production.

For investigation questions (metric drop or spike): Rule out data issues first, then segment by geography, platform, user type, and time. Form hypotheses before you go digging.

Lyft Data Scientist interviewers typically look for four things, based on what candidates report:

Product intuition tied to data. Lyft is a marketplace with riders, drivers, and the platform itself. Interviewers want to see that you think about metrics from multiple stakeholder angles, not just one side.

Rigorous experimentation thinking. A/B testing is central to how Lyft makes product decisions. Be ready to discuss statistical power, interference effects (one rider's behaviour affecting another), and when not to run an experiment.

Clean, efficient SQL. Take-home assessments and live coding rounds both test SQL. Comfort with window functions and aggregations is expected at all levels.

Communication that non-technical stakeholders can follow. Candidates who can explain a causal inference result in plain language score higher than those who only show technical depth. Lyft product managers and operations teams work closely with Data Scientists day to day.

Week 1: Foundations
Revise core statistics: hypothesis testing, confidence intervals, p-values, and common mistakes in A/B testing (peeking, multiple comparisons). Practise a solid set of SQL problems at medium-hard level on a coding platform, focusing on window functions.

Week 2: Product and Domain
Study Lyft's product deeply. Read about their pricing model, driver supply challenges, and any publicly reported product launches. For each product area, think about what metric you would use to measure success and what could go wrong.

Week 3: Machine Learning and Case Studies
Review supervised learning, model evaluation metrics, and class imbalance handling. Do 3-4 end-to-end case study walkthroughs: pick a Lyft feature, define the problem, propose a model, and define evaluation criteria.

Week 4: Mock Interviews and Behavioural Prep
Do at least 2 mock interviews with a peer or coach. Prepare STAR stories for 5-6 scenarios: disagreeing with stakeholders, shipping something that did not work, working with messy data, influencing without direct authority. Record yourself and watch it back.

During this period, knok checks 150+ job sites nightly, applies to new Lyft Data Scientist listings that match your resume, and messages HR for you, so you never miss a fresh opening while you are busy preparing.

Jumping to the model before defining the problem. Interviewers consistently flag candidates who start talking about specific algorithms before they have even defined what they are predicting and why. Frame the problem first.

Ignoring the two-sided marketplace. Lyft has riders and drivers. A metric that optimises for one at the expense of the other is usually a red flag. Always consider both sides.

Treating A/B tests as simple. Network effects, novelty effects, and interference are real concerns in a rideshare platform. Saying 'just run an A/B test' without acknowledging these issues gets noticed by interviewers.

Writing SQL that works but is hard to read. Correctness is necessary but not sufficient. Interviewers look at style and clarity, especially in take-home assessments.

Vague STAR answers. Saying 'my team improved the model' instead of specifying your individual contribution and the concrete outcome is a common reason candidates do not advance past the behavioural round.

Not asking clarifying questions. On ambiguous questions like 'design a fraud detection system,' candidates who jump straight to answers often miss key constraints. Ask about scale, latency, false positive tolerance, and available data features.