DoorDash Data Scientist Interview Guide: Questions, Case Studies & Prep Tips

Looking to join one of the most data-driven consumer platforms in the world? The DoorDash data scientist interview is designed to assess both your technical mastery and your ability to drive product impact at scale. From logistics optimization to pricing experiments, data scientists here do more than just crunch numbers—they shape the direction of the marketplace.

In this guide, we’ll walk you through the DoorDash data scientist interview process, including sample questions, preparation strategies, and what to expect across each stage.

Role Overview & Culture

At DoorDash, data scientists are embedded in cross-functional teams alongside engineers, designers, and product managers. They’re responsible for building and validating forecasting models, defining KPIs, running A/B tests, and providing recommendations that influence major product decisions. The culture thrives on bottom-up ownership, rapid experimentation, and customer-centricity, giving data scientists end-to-end visibility from analysis to deployment.

Why This Role at DoorDash?

The role offers tremendous scope: you’re analyzing the behaviors of millions of users, informing critical logistics decisions, and contributing to the core profitability of the business. With generous compensation, broad product exposure, and the ability to own your metrics and models, this position is ideal for data scientists who want impact—not just dashboards. Below, we’ll break down the DoorDash data scientist interview process so you can prepare with confidence.

The interview process for DoorDash data scientists is structured to evaluate technical ability, product thinking, and stakeholder communication. It typically consists of five stages, each assessing a different skillset.

Recruiter Screen

This 30-minute conversation is focused on your background, motivations, and fit for the team. Expect questions about past project impact and team collaboration.

Technical Phone Screen

This round tests your SQL fluency and statistics knowledge. You’ll be given a data problem to solve live using joins, aggregations, and filtering logic, followed by a few questions on A/B testing and probability.

Take-Home / Case Study

This stage evaluates your ability to analyze data in context. You may be asked to complete a metrics deep dive or propose improvements based on experimentation outcomes. Deliverables often include SQL queries, charts, and a short write-up or slide deck.

Virtual On-Site Loop

Typically includes 3–4 rounds across SQL coding, product case analysis, modeling, and a behavioral interview. You’ll walk through how you’ve impacted business decisions, led cross-functional efforts, or scaled experiments.

Hiring Committee

After your interviews, your performance is evaluated by a hiring committee. They assess interview feedback, case study submissions, and culture alignment before making a final decision.

Before diving into the specific topics, it’s important to note that DoorDash data science questions are highly product-driven. They want to see how you think about data in the context of real business trade-offs.

SQL / Data Manipulation Questions

These questions evaluate your ability to extract insights from delivery, user, and merchant datasets. Most DoorDash SQL interview questions simulate realistic scenarios from their logistics or growth org. Expect to write efficient queries using CTEs, subqueries, CASE WHEN, and date logic. Many DoorDash SQL questions also test edge-case handling and query optimization.

1. How can you select a truly random row from a 100-million-row table without throttling the database?

   DoorDash uses this prompt to test your grasp of sampling strategies that won’t trigger a full table scan. Strong answers compare approaches such as block-level sampling (TABLESAMPLE), ID-range randomization with sparse retries, and hash-based reservoir methods—highlighting trade-offs in uniformity, latency, and index reliance when operating on petabyte-scale OLAP clusters.

2. Given a bank-transactions table, how would you return the last transaction of each day (ID, timestamp, amount) in chronological order?

   This exercise checks fluency with window functions: partitioning by the truncated date, ordering by created_at DESC, and filtering on ROW_NUMBER = 1. Interviewers listen for details on date-time casting, time-zone safety, and indexing (created_at DESC) to keep the sort in memory even when DoorDash holds billions of payment events.

3. How would you calculate total distance travelled by every user and sort the results from highest to lowest?

   A correct query sums distance per user_id, groups, and orders descending. Strong candidates discuss handling canceled rides, null distances, and the benefits of date partitioning or clustering on (user_id, ride_date) for faster scans—skills directly applicable to courier-trip analytics at DoorDash scale.

4. Identify customers who made more than three purchases in both 2019 and 2020—what SQL pattern solves this efficiently?

   The problem combines conditional aggregation and set intersection. Interviewers expect a solution that aggregates yearly counts in a CTE, pivots them to columns, and filters where both exceed three. Mentioning indexes on (customer_id, order_date) and incremental ETL materializations demonstrates data-science pragmatism.

5. How would you list the top-three salaries by department—even when a department has fewer than three employees?

   Candidates need to use window functions with DENSE_RANK() partitioned by department, then filter on rank ≤ 3. Extra credit for explaining why department head-count filters (≥ 1) avoid empty output, and for discussing how salary distributions feed compensation-equity studies.

6. Create a histogram of comments per user for January 2020, placing everyone else in a zero bin. What does the final SQL look like?

   This tests binning logic: left-joining daily comment counts to January’s user base, defaulting nulls to zero, and grouping by bucket size. Discussing the impact of a class-interval-one histogram on skewed data and potential use of pre-computed comment fact tables shows an analytics mindset.

7. Write a query that selects the second-highest salary in Engineering, correctly handling ties at the top.

   Expect to rank salaries with DENSE_RANK() inside the Engineering partition and filter where rank = 2. DoorDash likes this classic because it distinguishes candidates comfortable with window functions from those who rely on fragile sub-queries.

8. How many customers were “upsold”—that is, bought an additional product after their first purchase (on a later day)?

   Good solutions identify each user’s first-purchase date via a window function, then filter for subsequent transactions. Explaining why same-day multiple purchases don’t count—and how date difference or > created_at logic enforces that rule—demonstrates nuanced funnel analytics thinking.

9. Create a new flight_routes table containing each unique city pair regardless of direction—how do you remove duplicates like “Dallas → Seattle” vs. “Seattle → Dallas”?

   The task checks your handling of unordered pairs: selecting LEAST(src, dst) and GREATEST(src, dst) to normalize direction before DISTINCT insertion. In a DoorDash context, similar logic underpins deduplicating symmetric address pairs or bidirectional courier zones.

10. Compute the engagement rate for every ad type, but only for users who viewed at least three distinct ad types in the last 48 hours. How would you structure the SQL?

    This combines time-window filtering, per-user distinct-count logic, and a click-through-rate calculation. Interviewers want to hear about indexed timestamp filters, grouping by ad_type, and the business rationale—removing low-exposure users yields more stable rate estimates used in DoorDash Ads bidding.

11. In a social-graph table of “likes”, how do you count each user’s number of liker’s likers (people who liked their likers)?

    Candidates need a self-join or CTE chaining likes twice, grouped per root user. A crisp explanation demonstrates comfort with recursive-style joins and graph-analytics basics—skills useful when DoorDash models referral networks or courier mentorship links.

Product / Experimentation Case Studies

Many candidates report a DoorDash data science case study round. You’ll often be asked to design or critique an experiment and explain what success looks like.

Strong candidates demonstrate a clear balance between statistical rigor and practical constraints in product environments.

1. How would you calculate the conversion rate for each variant in a trial-period A/B test when the control counts any post-experiment subscription and the trial counts only subscriptions that persist seven days or more?

   The task requires conditional conversion logic inside a single query. For the control rows you look for any subscription event after assignment; for the trial rows you add a survival clause (no cancellation, or cancellation ≥ 7 days later). A common solution aggregates in a CASE statement keyed on variant, yielding converted ÷ total for each arm. Interviewers listen for clarity on windowing (entry time vs. subscription time) and how that choice affects bias when trial users churn quickly. Discussing confidence intervals and a chi-square or G-test to compare rates shows awareness that metric computation is only half the story.

2. Instagram’s “close friends” story list creates network interference. How would you build control and test groups that account for spillover effects?

   A naïve random split fails because treatment for one user changes the feed of their friends. Strong answers outline cluster randomization—e.g., assigning entire ego networks or tightly knit communities to the same arm—or two-stage experiments that treat at the friend-list level and measure at the user level. You’d also describe measuring exposure (what fraction of a user’s friends is treated) and using propensity-weighted estimators to recover an average treatment effect under partial interference.

3. Should Facebook copy Instagram Stories? Design an experiment and analytic framework to decide.

   Begin with a crisp success metric such as incremental daily active minutes, ad impressions, or cannibalization of feed posts. Propose a phased rollout—perhaps geography-level randomization—to avoid network contamination. Secondary metrics monitor negative externalities like slower page loads or crowding out of core content. A compelling answer notes that stories may shift, not add, engagement; therefore you’d run mediated analyses to see where time is re-allocated and whether revenue per minute rises.

4. An Uber Fleet A/B test yields non-normal metric distributions because of small sample sizes. Which statistical methods would you use and how would you declare a winner?

   Candidates should mention non-parametric approaches—Mann-Whitney U, bootstrap confidence intervals—or Bayesian methods that do not rely on asymptotic normality. You might also log-transform skewed metrics or compare medians. DoorDash likes when candidates explain the practical implication: wide tails inflate variance, so detecting lift requires either longer tests or metrics such as 75th-percentile latency that better approximate normality.

5. Robinhood tested a “market-open” push to one million active users. How would you evaluate the notification’s impact?

   Outline primary metrics (session starts within 10 minutes, trade count, retention after a week) and ensure a holdout group with similar pre-experiment engagement. Given push notifications can annoy users, call out opt-out and uninstall rates as guardrails. If exposure time varies by user, consider causal-impact or difference-in-differences models to isolate incremental behaviors beyond seasonality.

6. You have four new marketing channels and a limited budget. How do you design an experiment to spend dollars efficiently while estimating each channel’s ROI?

   Describe allocating budget with a multi-armed-bandit or Thompson-sampling framework that shifts spend toward higher-ROI channels in real time. Alternatively, run a stratified geo-lift test, randomizing DMAs to treatments and using synthetic-control baselines. Emphasize measuring not just immediate conversions but cross-channel spillovers and lifetime value, then feeding results into a budget-allocation model for future campaigns.

7. Facebook plans to replace the post composer with a “+” button. How would you test if this is a good idea without hurting core posting metrics?

   You’d randomize at user level, track primary KPIs like posts per user and time to first post, and set guardrails on session length. Because UI changes can shift usage between mobile and web, segment results by platform. To mitigate novelty bias, plan a multi-week test and monitor leading indicators (clicks on the new button) as well as lagging outcomes (retention, ad revenue per session).

8. Prime Video wants to pre-launch a show to 10 000 users. How do you pick the sample and measure performance?

   Stratify by viewing genre affinity, engagement level, and device type to ensure the pilot cohort mirrors the target audience. Use a wait-listed control to account for selection bias: everyone signs up, but only some get access. Evaluate incremental watch-through rate, new-subscriber lift, and churn prevention over a 30-day horizon. Discuss how early buzz could leak, so you might embargo social-sharing or monitor spillover in the control group.

9. One A/B cell has 50 K users, the other 200 K. Does the unbalanced size bias results?

   Unequal allocation does not bias the estimator if assignment is random, but it widens confidence intervals for the smaller arm and can reduce power. Mention variance-weighted tests or CUPED to regain efficiency. If the traffic imbalance stems from capacity limits (e.g., server cost), you’d explain how to plan minimum-detectable-effect calculations under unequal splits.

10. Your manager ran a test with 20 variants and found one “significant.” What concerns do you have?

    The obvious issue is multiple-testing. Explain family-wise error inflation and propose Bonferroni, Holm, or false-discovery-rate controls. Also question whether the metric was peeked mid-test; sequential testing procedures like alpha spending or Bayesian power curves might be more appropriate.

11. Compare user-tied versus instance-tied randomization—what are the pros and cons?

    User-tied tests maintain treatment consistency—which is ideal for personalization and limits contamination—but converge slowly when per-user traffic is sparse. Instance-tied tests randomize at impression level, boosting sample size and power, but risk cross-talk if users see both variants; this can dilute treatment effects, especially for UI changes that rely on habituation.

12. How would you gauge the impact on teen engagement when their parents join Facebook?

    Because randomizing parental sign-ups is impossible, propose a quasi-experimental design: difference-in-differences comparing teens whose parents joined during the window to a matched cohort whose parents didn’t. Control for pre-trend engagement and demographic covariates using propensity scores. Secondary analyses could segment by parent engagement level to see whether lurkers or active posters drive changes in teen behavior.

Modeling & Machine Learning Questions

These are designed to test your knowledge of modeling techniques and how you’d apply them in production. You might be asked to choose between regression vs. classification, explain feature engineering decisions, or outline a modeling pipeline.

These DoorDash data science interview questions may appear in the take-home round or during the on-site loop.

1. What is the core difference between Lasso and Ridge regression, and how does that choice affect your feature set?

   Lasso (L1) adds absolute-value penalties that can drive small coefficients exactly to zero, effectively performing embedded feature selection and yielding a sparse model that is easier to interpret. Ridge (L2) uses squared penalties, shrinking coefficients toward—but not exactly to—zero, which reduces variance without eliminating variables. In practice you reach for Lasso when you suspect many irrelevant predictors, and Ridge when multicollinearity inflates variance but you still want to keep all signals in play. Elastic Net combines both to balance sparsity and group-shrinkage, making it a common default in high-dimensional problems.

2. How would you interpret logistic-regression coefficients for categorical and Boolean predictors?

   In a logistic model each coefficient represents the log-odds change of the outcome for a one-unit increase in that predictor while holding others constant. For Boolean variables (0/1) the exponentiated coefficient is the odds ratio between the two groups. For k-level categoricals you choose a reference category; each dummy coefficient then compares its level’s odds to the reference. Communicating odds ratios—or converting them to marginal probability changes at typical baselines—helps non-technical stakeholders grasp practical impact.

3. Given historical keyword prices, how would you build a model that bids on an unseen keyword?

   Begin with feature engineering: tokenize the phrase, derive TF-IDF or embedding vectors, cluster by topical similarity, and include contextual signals like geographic CPC or seasonality buckets. A gradient-boosted tree or Bayesian regression can predict a reserve bid, with uncertainty intervals guiding aggressiveness. In production you’d retrain nightly, cap bids with business rules, and A/B the model against manual bidding to quantify lift in cost-per-acquisition.

4. A loan-approval model must explain individual rejections, but raw feature weights are unavailable. How do you generate applicant-specific reasons?

   Use model-agnostic explainers such as SHAP or LIME to approximate local feature contributions for each prediction, then map the top positive contributors (those pushing toward rejection) to human-readable rules—e.g., “Debt-to-income ratio too high.” You’d pre-compute explanations during batch scoring, store them alongside decisions, and audit them for fairness to ensure protected attributes are not invoked indirectly.

5. Compare the logistic and softmax functions. Why are both useful in logistic regression and multi-class settings?

   The logistic (sigmoid) squashes real numbers to (0,1) and models binary class probability. Softmax generalizes this idea, converting a vector of K logits into a probability distribution that sums to 1—making it the default final layer for multi-class neural nets. In binary logistic regression a sigmoid is sufficient, but framing it as two-class softmax yields the same result and scales naturally when you later extend to K>2 categories.

6. How would you build an automated system to detect firearm listings on an e-commerce marketplace?

   Combine a text classifier using keyword embeddings and transformer features with an image classifier fine-tuned on firearm photos. Fuse scores in an ensemble or rule-based threshold, and add a similarity search against a curated gun catalog. A human-in-the-loop queue reviews borderline cases, feeding back labels for continual learning. Latency constraints decide whether light models run at upload time and heavier CNNs operate asynchronously.

7. You have one million ride-share trips from Seattle and want to predict ETA. How can you tell if that data volume is sufficient?

   Plot learning curves of error versus training-set size; if the curve flattens well before one million, more data offers diminishing returns. Check feature coverage: rare routes or rush-hour pockets may still be under-represented. Perform cross-validation by geography and time to see if generalization gaps emerge. Finally, simulate additional noise to estimate whether variance or bias dominates; if bias plateaus, focus on features rather than more rows.

8. Why is bias measurement especially important when modeling food-preparation times for restaurants?

   Preparation estimates can systematically skew longer for certain cuisines or newer merchants, leading to unfair courier wait times and order cancellations. Measuring bias across cuisine, order size, and kitchen capacity surfaces equity issues and reveals target leakage (e.g., using driver arrival time in features). Without bias checks, models might penalize small or minority-owned restaurants, contradicting marketplace fairness goals.

9. Design an end-to-end pipeline that ingests hourly subway ridership data and outputs station-level forecasts updated in real time.

   Raw turnstile feeds land in a streaming buffer, then aggregate into time-series features (lagged counts, weather, holidays) in a feature store. A global Prophet or LSTM model with station embeddings retrains nightly, while a lightweight Kalman filter adjusts predictions hourly. A gRPC prediction service serves low-latency requests; model versioning, drift monitors, and SLA dashboards satisfy the client’s reliability contracts.

10. You must balance public posts and private friend content in a newsfeed. How would you model the ranking and what metrics ensure the right mix?

    Create hierarchical features: user–post interaction history, author social distance, freshness decay, and content type. Train a multi-objective ranker that maximizes watch-time and interaction while adding a diversity regularizer penalizing over-dominance of either public or private sources. Key metrics include session depth, share rate, and a calibration metric tracking public/private ratio versus a target band; offline NDCG and online A/B tests validate improvements.

11. A key numeric predictor in a logistic model suffered a data-quality issue—some values lost their decimal points. Is the model still valid, and how would you fix it?

    The model likely misclassifies because the corrupted values lie far outside the training range, breaking the learned linear relationship and potentially saturating the sigmoid. Detect the anomaly via summary stats or KS tests, repair by rescanning raw logs to restore decimals, or winsorize outliers if recovery is impossible. Retrain the model and add validation rules in the feature pipeline to catch future scaling mishaps.

Behavioral & Stakeholder Alignment Questions

In the DoorDash data science interview, it’s not just about technical ability—it’s about how well you collaborate. Expect questions on how you’ve influenced product decisions, aligned on metrics, or resolved ambiguity across teams.

Strong candidates bring structure to unstructured problems and clearly articulate the “so what” behind their analysis.

1. Describe a data project you worked on. What challenges did you face, and how did you overcome them?

   DoorDash wants evidence that you can steer an analytics or ML project from messy inception to measurable impact. A winning story sets the scene (scale, stakeholders, deadlines), names two or three obstacles—missing features, schema drift, unexplained metric spikes—and walks through the concrete actions you took to unblock progress. Close by quantifying the result (e.g., “reduced order-denial rate by 12 %”) and highlighting any process improvements that became team norms.

2. What techniques have you used to make complex data or model outputs accessible to non-technical partners?

   Great answers pair a specific audience (ops managers, merchants, finance) with a tailored artifact—interactive Tableau dashboards, plain-language OKR scorecards, or SHAP-based visual explainers. Emphasize iterative feedback cycles and how better accessibility shortened decision time or cut follow-up questions, proving that clarity accelerates DoorDash’s bias-for-action culture.

3. What would your current manager say are your greatest strengths and areas for improvement, and how have you acted on that feedback?

   Pick strengths that map to DoorDash’s needs (e.g., “rapid prototyping of causal-impact models”) and back them with outcomes. For a genuine growth area—perhaps delegating low-level tasks—explain concrete steps you’re taking, such as adopting project-tracking rituals or pairing with junior analysts. Framing weaknesses as actively managed learning goals signals self-awareness, not deficiency.

4. Tell us about a time you had trouble communicating with stakeholders. How did you bridge the gap?

   Focus on a real incident—say, merchandising leaders misinterpreting A/B-lift confidence intervals. Outline how you diagnosed the misunderstanding, switched to simpler visuals or analogies, and scheduled bite-size checkpoints that rebuilt trust. End with a metric showing improved collaboration, such as faster experiment sign-offs or fewer revisions.

5. Why do you want to work at DoorDash, and why in a data-science capacity?

   Tie your motivation to DoorDash’s mission of empowering local economies, then connect your skills—demand forecasting, courier-routing models, causal inference on promotions—to problems in the job description. Mention a recent initiative (e.g., Project Dash or DoubleDash) and describe how you could push its metrics forward, demonstrating that you’ve done homework beyond corporate boilerplate.

6. Describe a time when an experiment you championed failed to confirm your hypothesis. How did you communicate the outcome and pivot next steps?

   DoorDash values intellectual honesty and rapid iteration. Highlight an A/B test or causal study that produced a null or negative lift, the analytical deep-dive you ran to verify validity, and the narrative you shared with product partners to convert “failure” into actionable learning. Stress how the insights informed a follow-up idea that eventually moved the needle.

7. Walk us through how you balance speed versus rigor when stakeholders push for immediate answers but the data is noisy or incomplete.

   Effective stories show you negotiating scope—delivering a directional cut within hours while outlining a plan for a statistically robust follow-up. Discuss gating metrics you refuse to shortcut (e.g., sample-size requirements for funnel drop-offs) and how clear communication of uncertainty kept leadership aligned without blocking fast product cycles.

The DoorDash data scientist interview is rigorous—but predictable. Candidates who prepare with real-world case studies, fast-paced SQL drills, and structured product thinking tend to outperform. Below are five targeted ways to level up your prep.

Master SQL Quickly

DoorDash SQL interview questions emphasize both correctness and efficiency. Practice with 60+ LeetSQL questions and timebox yourself to 15 minutes per query. Focus on joins, window functions, and funnel analysis.

Simulate DoorDash Case Studies

Rebuild real-world business cases involving delivery times, demand forecasting, or marketplace pricing strategies. The best way to prep for a DoorDash data scientist interview case study is by applying your insights to ambiguous data problems—just like the ones you’d face on the job.

Practice Experiment Design

You’ll likely face product sense questions around A/B testing and metric design. Focus your preparation on key performance areas such as Dasher pay, retention, and order conversion. These are staples in the DoorDash data science case study round.

Communicate Trade-Offs

Interviewers want to see how you think. Use the STAR framework in behavioral rounds and always clarify assumptions before writing SQL or outlining a model. Strategic communication is just as important as technical ability in the DoorDash data scientist interview.

Mock Interviews & Feedback

Run practice interviews with peers or past DoorDash candidates. Use Interview Query’s mock platform to simulate pressure and refine your timing. These sessions are crucial if you’re aiming to land a data scientist DoorDash offer.

What Is the Average Salary for a Data Scientist at DoorDash?

DoorDash offers highly competitive compensation across levels. Salary bands typically vary by level (L3–L6) and location (e.g., SF vs. remote). Expect strong base + RSU packages.

Are DoorDash Data Scientist Jobs Listed on Interview Query?

Yes—visit our DoorDash job board to see active listings. Sign up to get alerts for data science roles, referrals, and insider prep resources.

Cracking the DoorDash data scientist interview comes down to structured, intentional preparation. From mastering SQL under time pressure to walking through ambiguous A/B test scenarios, the strongest candidates treat each round like a real product problem. Focus on articulating trade-offs, practicing real case studies, and aligning your insights with DoorDash’s business metrics.

If you’re ready to take the next step, check out our mock interviews to simulate live pressure, or try our AI Interviewer for instant feedback. You can also explore curated learning paths designed for data science candidates targeting marketplace companies. For added inspiration, read how Hoda Noorian successfully navigated the interview process with Interview Query.