Entry Level Data Scientist Interview Preparation Guide - FAANG Standards

Data Scientist

entry

6 rounds

Updated 6/22/2026

This guide is based on general FAANG interview practices and may not reflect specific company procedures.

The Entry Level Data Scientist interview process at FAANG companies typically consists of 6 rounds spanning 4-8 weeks. The process evaluates your foundational technical skills in SQL and Python, understanding of statistics and experimentation, ability to apply data insights to business problems, and culture fit. Rounds progress from initial screening through multiple technical assessments to final behavioral evaluation. The focus is on demonstrating learning potential, clear communication of problem-solving approach, and fundamental competency in core data science tools and concepts.

Interview Rounds

Recruiter Screening

30 min4 focus topicsbehavioral

What to Expect

Your first interaction with the company's hiring team. A recruiter will assess your background, technical foundation, motivation for joining the company, and cultural fit. They'll confirm you meet basic educational and experience requirements, discuss your relevant projects or internships, and verify your proficiency with core tools like SQL, Python, and statistics. This round determines whether you advance to technical interviews.

Tips & Advice

Prepare a 2-3 minute elevator pitch about your background, highlighting any data-related projects, internships, or coursework. Research the company and articulate genuine reasons for wanting to join beyond just the brand name. Have 1-2 concrete examples ready that demonstrate your technical skills and enthusiasm for data science. Use keywords from the job description like SQL, Python, A/B testing, machine learning, and data visualization. Be authentic about your skill level - recruiters appreciate honesty over exaggeration.

Focus Topics

Handling Questions About Gaps or Weaknesses

Being prepared to discuss any gaps in experience honestly (e.g., 'I haven't worked with TensorFlow professionally, but I'm familiar with scikit-learn and eager to learn'). Framing learning opportunities positively. Showing self-awareness about areas for growth.

Practice Interview

Study Questions

Motivation and Culture Fit

Articulating genuine reasons for interest in the company and role. Showing awareness of the company's mission, products, or impact. Demonstrating values alignment (e.g., curiosity, collaboration, learning mindset, drive for impact). Being able to describe your work style and how you collaborate with others.

Practice Interview

Study Questions

Background and Experience Communication

Effectively articulating your educational background, internships, academic projects, or personal projects with data. Being able to briefly explain what you learned and how it relates to the Data Scientist role. Demonstrating awareness of the role and company.

Practice Interview

Study Questions

Technical Proficiency Confirmation

Demonstrating familiarity with core Data Scientist tools: SQL (basic queries), Python (data manipulation), statistics basics, and data visualization. Understanding the difference between correlation and causation, knowing what A/B testing is, and being able to explain your experience with machine learning frameworks if applicable.

Practice Interview

Study Questions

Technical Phone Screen

60 min5 focus topicstechnical

What to Expect

A 45-60 minute virtual interview conducted via video call using a shared code editor. This round assesses your fundamental SQL and Python skills through live coding and data manipulation problems. You'll work on problems that simulate real data challenges, such as filtering datasets, calculating metrics, and joining tables. The interviewer will evaluate your ability to write working code, ask clarifying questions, and explain your approach. Efficiency, readability, and correctness all matter, but the evaluation emphasizes your problem-solving process and communication over perfect code.

Tips & Advice

Before writing code, take 1-2 minutes to understand the problem thoroughly by asking clarifying questions about data, expected output, and constraints. Outline your approach in pseudocode or verbally before implementing. Start with a working solution even if not optimal - correctness matters more than efficiency for entry level. Explain your logic as you write code. After finishing, walk through your solution step-by-step and check for edge cases like null values, empty datasets, or boundary conditions. If you get stuck, verbalize what you're thinking and ask for hints - interviewers appreciate honest communication. Write clean, readable code with meaningful variable names.

Focus Topics

Code Efficiency and Readability

Writing code that is readable with meaningful variable names and comments. Understanding time and space complexity at a basic level. Recognizing when a solution might be inefficient (e.g., nested loops instead of vectorized operations). Optimizing for clarity first, then efficiency.

Practice Interview

Study Questions

Edge Cases and Data Quality

Considering null/missing values, empty datasets, duplicate records, and outliers. Validating data before analysis. Handling different data types (integers, floats, strings, dates). Understanding how data quality issues affect analysis results. Testing solutions with edge cases.

Practice Interview

Study Questions

Problem-Solving Process and Communication

Asking clarifying questions before jumping into coding. Thinking aloud through your approach. Explaining the logic behind your solution. Handling confusion or mistakes gracefully. Requesting hints when stuck. Collaborating with the interviewer rather than treating it as a test to pass alone.

Practice Interview

Study Questions

Python Data Manipulation with Pandas and NumPy

Using Python libraries (pandas, NumPy) to manipulate datasets. Filtering rows and columns, handling missing values, merging/joining dataframes, calculating aggregations, working with different data types. Writing functions and loops. Debugging code. Basic string operations and working with lists/dictionaries. Understanding vectorization for efficiency.

Practice Interview

Study Questions

SQL Fundamentals

Writing basic SQL queries including SELECT, WHERE, GROUP BY, HAVING, ORDER BY clauses. Understanding different JOIN types (INNER, LEFT, RIGHT, FULL OUTER). Aggregating data with functions like COUNT, SUM, AVG, MAX, MIN. Calculating metrics like daily active users, retention, conversion rates. Filtering on multiple conditions. Basic understanding of indexes and query efficiency.

Practice Interview

Study Questions

Coding Interview - Data Manipulation and SQL

60 min5 focus topicstechnical

What to Expect

A 45-60 minute technical interview conducted on-site or virtually with a data scientist or analyst. This round goes deeper into SQL and Python coding than the phone screen, typically with 2-3 problems of moderate complexity. Problems often simulate real data science scenarios: combining multiple datasets, calculating complex metrics, handling data quality issues, or performing exploratory analysis. The interviewer assesses your problem-solving approach, ability to work with imperfect data, and how clearly you can explain your reasoning.

Tips & Advice

Start each problem by clarifying the requirements and edge cases. Break complex problems into smaller steps - solve the problem correctly first, then optimize if needed. For SQL problems, mentally trace through the query logic before executing. For Python, test your code mentally with sample data. Draw diagrams if helpful (e.g., sketching how to join tables). If stuck on one problem, move forward rather than spending too long. It's better to attempt two problems partially than spend all time on one. Explain your trade-offs when choosing between different approaches (e.g., 'I could use a nested loop which is simpler, or vectorize with NumPy which is faster').

Focus Topics

Problem Analysis and Approach Selection

Breaking down a data problem into steps. Identifying what information is available and what needs to be derived. Choosing between SQL vs Python vs a combination for different problems. Recognizing when different approaches are more appropriate.

Practice Interview

Study Questions

Handling Data Quality Issues

Identifying and handling missing values, duplicates, outliers, and inconsistent data types. Deciding when to filter out bad data vs when to impute or transform it. Understanding how data quality decisions affect analysis results. Validating your output against expectations.

Practice Interview

Study Questions

Metric Calculation and Aggregation

Calculating business metrics like conversion rates, customer lifetime value, daily/weekly active users, churn rate, retention cohorts, and funnel analysis. Aggregating data at different levels (daily, weekly, user, segment). Using GROUP BY correctly with filtering. Understanding when to use HAVING vs WHERE.

Practice Interview

Study Questions

Advanced SQL Techniques

Writing complex queries with multiple JOINs, subqueries, and window functions (RANK, ROW_NUMBER, LAG, LEAD). Calculating running totals, moving averages, and consecutive event detection. Using CTEs (Common Table Expressions) for readability. Understanding UNION vs UNION ALL. Self-joins and complex filtering logic.

Practice Interview

Study Questions

Data Joining and Merging Strategies

Combining multiple tables or dataframes correctly using different join types. Understanding the implications of each join type (INNER, LEFT, RIGHT, FULL OUTER, CROSS). Handling many-to-many relationships and duplicate records. Identifying and resolving join keys. Dealing with mismatched or missing keys.

Practice Interview

Study Questions

Statistics and Experimentation Interview

60 min5 focus topicstechnical

What to Expect

A 45-60 minute technical interview conducted by a data scientist or analytics specialist. This round tests your understanding of statistics, hypothesis testing, A/B testing, and experimental design. You'll likely be given a scenario like 'We want to test a new recommendation feature. How would you design an A/B test to measure its impact?' or 'How would you determine if a change in a metric is statistically significant?' The round evaluates your conceptual understanding of statistical thinking, ability to balance rigor with practical constraints, and how you translate business questions into statistical frameworks.

Tips & Advice

Don't feel pressured to solve everything with complex math - often the interviewer wants to see your thinking process more than statistical formulas. When presented with a business question, first clarify what you're trying to learn and why it matters. Design experiments that are practical and interpretable, not just theoretically perfect. Explain the purpose of each element (e.g., 'I'm using a control group so we can isolate the effect of the new feature'). Be honest about limitations of your approach. If you're unsure about a statistical concept, ask clarifying questions or reason through it logically. Entry-level candidates aren't expected to know everything - interviewers value clear thinking over perfect knowledge.

Focus Topics

Interpreting and Communicating Results

Explaining what a p-value means in plain language. Communicating confidence intervals and what they represent. Describing results to non-technical stakeholders. Recognizing when statistical significance differs from practical significance. Highlighting limitations and caveats in findings.

Practice Interview

Study Questions

Experiment Design and Practical Considerations

Defining metrics to measure (primary and secondary). Estimating sample size and duration needed. Choosing between between-subjects and within-subjects designs. Considering confounding variables and how to control for them. Planning for randomization strategy. Discussing trade-offs between statistical rigor and practical constraints.

Practice Interview

Study Questions

Common Statistical Distributions and Concepts

Understanding normal distribution, binomial distribution, and Poisson distribution. Knowing when each applies. Understanding concepts like mean, median, standard deviation, variance. Understanding correlation vs causation. Basic probability concepts (conditional probability, independence).

Practice Interview

Study Questions

A/B Testing Fundamentals

Understanding the purpose of A/B tests (isolating causality). Key components: hypothesis, control and treatment groups, randomization, sample size, and statistical significance. Calculating required sample size. Interpreting p-values and confidence intervals. Recognizing when A/B testing is appropriate vs other methods. Avoiding common pitfalls like peeking at results early or not randomizing properly.

Practice Interview

Study Questions

Hypothesis Testing and Statistical Significance

Understanding null and alternative hypotheses. Selecting appropriate statistical tests (t-test, chi-square, z-test) based on data type and question. Interpreting p-values (not as probability a result is correct, but probability of observing this data if null hypothesis is true). Understanding Type I and Type II errors. Determining significance level (alpha). Understanding power analysis at a basic level.

Practice Interview

Study Questions

Product Sense and Case Study Interview

60 min5 focus topicscase study

What to Expect

A 45-60 minute technical interview conducted by a product manager or senior data scientist. This round evaluates your ability to apply data-driven thinking to business problems. You'll receive scenarios like 'How would you measure the success of a new feature?' or 'Our user retention is dropping - how would you investigate and what would you do?' The interviewer assesses how you frame business problems, identify relevant metrics, design approaches to answer questions, and communicate findings. This round tests judgment and business acumen alongside technical skills, showing whether you can work effectively on cross-functional teams.

Tips & Advice

Start by asking clarifying questions about the business problem, context, and constraints rather than jumping to solutions. For metric questions, think about leading indicators, lagging indicators, and trade-offs between them. Break complex problems into subquestions you can answer with data. Use your SQL/Python knowledge to suggest how you'd actually measure things. Discuss trade-offs in your approach (e.g., 'This metric is easier to measure but might not capture the full picture'). Don't assume the first idea is right - discuss alternatives and their pros/cons. Relate the analysis back to business impact - why does this matter? Entry-level candidates won't have extensive product experience, so interviewers value structured thinking more than deep product knowledge.

Focus Topics

Communication and Storytelling

Explaining your analysis approach in terms a product manager or executive would understand. Highlighting key findings concisely. Structuring findings to tell a coherent story. Acknowledging assumptions and limitations. Recommending actions based on insights.

Practice Interview

Study Questions

Feature Success Measurement

Designing measurement plans for new features including primary and secondary metrics. Defining guardrail metrics to ensure you're not optimizing for something at the expense of something else. Understanding ramp-up and stabilization periods for new features. Considering different user segments and whether feature affects them differently.

Practice Interview

Study Questions

Applying Data Science Techniques to Business Problems

Recognizing when A/B testing is appropriate vs observational analysis. Suggesting SQL queries or analysis approaches that would answer the business question. Understanding when you'd need advanced modeling vs simpler analysis. Connecting technical approaches to business decisions (e.g., 'This cohort analysis would tell us whether the decline is from existing users or new users').

Practice Interview

Study Questions

Business Metrics and KPI Definition

Identifying appropriate metrics for different business questions (e.g., engagement vs satisfaction vs monetization). Understanding leading vs lagging indicators. Choosing between absolute values, rates, and ratios depending on context. Defining metrics precisely (e.g., what counts as a daily active user?). Understanding metric trade-offs (e.g., optimizing for engagement might reduce monetization).

Practice Interview

Study Questions

Problem Framing and Root Cause Analysis

Breaking down vague problems into specific, answerable questions. Distinguishing between symptoms and root causes. Suggesting hypotheses for why something is happening. Outlining how you'd investigate using data. Building diagnostic funnels to isolate issues (e.g., if revenue is down, is it user acquisition, retention, or monetization?). Structuring problems in a way that suggests next steps.

Practice Interview

Study Questions

Behavioral and Culture Fit Interview

60 min5 focus topicsbehavioral

What to Expect

A 45-60 minute interview, often with a hiring manager or senior team member, focused on your background, work style, learning approach, and cultural fit. You'll discuss past projects, how you handle challenges, how you work with others, and your motivation for the role. The interviewer evaluates your curiosity, ability to learn, resilience, teamwork, and alignment with company values. For entry-level candidates, this round emphasizes learning potential, coachability, and collaborative mindset over deep experience.

Tips & Advice

Prepare concrete stories using the STAR method (Situation, Task, Action, Result) that illustrate how you learn, collaborate, handle challenges, and contribute to teams. Focus on projects or situations that show growth and learning rather than just successes. Be genuine - interviewers can tell when answers are rehearsed. Discuss how you handle feedback and failure. Show curiosity about the role and company beyond just the job responsibilities. Ask thoughtful questions about the team, culture, and what success looks like in the first 6-12 months. For entry-level, emphasizing learning ability and coachability matters more than years of experience.

Focus Topics

Handling Feedback and Criticism

Sharing an example of receiving critical feedback and how you responded. Discussing how you use feedback to improve. Showing growth from past mistakes. Explaining how you balance confidence with humility and openness to being wrong.

Practice Interview

Study Questions

Problem-Solving Under Uncertainty

Discussing situations where you didn't know the solution upfront and how you approached figuring it out. Sharing examples of overcoming obstacles or challenges on projects. Explaining how you break down ambiguous problems. Showing comfort with trying different approaches when the first one doesn't work.

Practice Interview

Study Questions

Motivation and Impact Orientation

Articulating genuine interest in using data to solve problems and drive impact. Discussing why data science appeals to you. Sharing examples of situations where insights or data changed decisions or outcomes. Showing understanding of how the data scientist role contributes to the company's mission.

Practice Interview

Study Questions

Learning Ability and Growth Mindset

Demonstrating through examples that you seek to learn and grow. Discussing technologies or techniques you've taught yourself. Sharing situations where you didn't know something and how you approached learning it. Showing comfort with being a beginner and willingness to ask for help. Explaining how you stay current with data science developments.

Practice Interview

Study Questions

Collaboration and Communication

Sharing examples of working effectively with teammates or on group projects. Discussing how you ask for help when stuck. Explaining how you explain technical concepts to non-technical people. Showing openness to feedback and different perspectives. Demonstrating ability to work with people who have different expertise.

Practice Interview

Study Questions

Frequently Asked Data Scientist Interview Questions

Problem Solving and Communication ApproachEasyTechnical

36 practiced

A stakeholder asks why not use a simple linear model instead of a complex neural net for a small dataset. Explain in plain language the trade-offs you would convey (overfitting risk, interpretability, maintenance cost), and what evidence you'd collect to support your recommendation.

Sample Answer

Situation: A stakeholder suggests using a simple linear model instead of a neural net because the dataset is small. I would explain trade-offs in plain language and propose evidence to decide.

Trade-offs to convey:- Overfitting risk: Neural nets have many parameters and can memorize small datasets, giving good training performance but poor real-world results. Linear models are less flexible, so they're less likely to overfit on limited data.- Interpretability: Linear models give clear coefficients you can explain to business users (e.g., “X increases outcome by Y”), while neural nets are largely black boxes unless you invest in post-hoc explanation techniques.- Maintenance and cost: Neural nets typically need more compute, monitoring, and skill to retrain and tune. That increases operational and personnel costs. Linear models are cheaper to run and easier to maintain.

Evidence I’d collect to support a recommendation:- Baseline comparison: Fit a regularized linear model (ridge/lasso) and a small neural net using the same features.- Robust evaluation: Use k-fold cross-validation and a held-out test set to compare out-of-sample metrics (e.g., RMSE, AUC). Report confidence intervals.- Learning curves: Plot performance vs. training size to see if the neural net improves with more data — if curves converge, a complex model may not help.- Overfitting checks: Compare train vs. validation performance; large gaps indicate overfitting.- Explainability checks: Show feature importances or partial dependence for the linear model and attempt SHAP or LIME for the neural net; quantify how actionable each is.- Cost assessment: Estimate compute, deployment complexity, and expected maintenance effort.

Recommendation approach:- Start with the simpler model as a baseline. If the neural net yields materially better and robust out-of-sample performance and the business justifies the extra cost/complexity, adopt it; otherwise choose the linear model for interpretability, speed, and lower maintenance.

Business Impact Measurement and MetricsMediumBehavioral

82 practiced

Tell me about a time you convinced stakeholders to change the evaluation window for an experiment because of business seasonality or delayed effects. Describe the evidence you used, how you balanced statistical power and timeliness, and how you communicated the change.

Data Quality and Edge Case HandlingMediumTechnical

70 practiced

Explain what a data contract is between producer and consumer teams in a data platform. Describe enforcement and monitoring mechanisms (schema evolution rules, schema registry, CI tests, runtime validators), how to define backwards-compatible changes, and a rollout plan to handle breaking changes in production.

Sample Answer

A data contract is an explicit agreement between producer and consumer teams that defines the structure, semantics, quality, and SLAs of data exchanged on the platform—think: schema, field types, nullability, required business semantics, update frequency, expected cardinality, and allowed evolution paths. It reduces downstream surprises and enables automated checks.

Enforcement & monitoring:- Schema registry: central store (Avro/Protobuf/JSON Schema) with versioning, compatibility settings. Producers must register new versions; consumers can pin to versions or accept compatible upgrades.- Schema evolution rules: adopt strict compatibility policy (e.g., backward, forward, full) per dataset. Example: for event topics use backward compatibility so old consumers can read new producers’ events.- CI tests: producers run unit/integration tests that validate produced sample messages against registered schemas, plus contract tests that assert required fields, types, and sample downstream queries.- Runtime validators: lightweight schema validation in ingestion (Kafka serializers/deserializers, API gateways) and data quality checks (row counts, null rates, value ranges) that emit metrics and alerts.- Monitoring: lineage + telemetry (schema version, producer ID) surfaced in dashboards; automated alerts on schema changes, QCs failing, or consumer deserialization errors.

Defining backwards-compatible changes:- Add optional nullable fields with defaults — backward-compatible.- Narrowing/renaming fields, removing fields, or changing types are breaking unless handled via aliases or converters.- Use semantic versioning for schemas and document compatibility guarantees in the contract.

Rollout plan for breaking changes:1. Design: propose change in contract doc, list impact matrix (consumers, queries).2. Communicate: notify consumers with timeline, migration guide, sample data, and test harness.3. Dual-write / shadow mode: producers emit both old and new schema (or include adapter) so consumers can test without disruption.4. Canary: enable change for a small subset of consumers or traffic; monitor errors/metrics.5. CI gate: require consumers to run integration tests against new schema before graduation.6. Cutover: after validation, flip default schema and deprecate old version with clear deprecation timeline.7. Rollback plan: keep ability to revert producer changes and coordinate cutback immediately if key metrics degrade.

As a data scientist I’d insist on clear semantic docs, automated contract tests in CI, and dashboards for schema/version adoption—these minimize surprises and let teams safely evolve data.

Collaboration and Communication SkillsEasyBehavioral

81 practiced

Describe a specific code or pipeline review you participated in on an ML project. How did you provide constructive feedback, handle disagreements about style or architecture, and how did you react when someone gave you critical feedback?

Sample Answer

Situation: At my last company I reviewed a pull request for a training pipeline that added a new feature store join and retraining workflow for a customer-churn model. The PR changed data-loading code, added a DAG task, and introduced a custom serialization helper.

Task: My goal was to ensure correctness, reproducibility, and maintainability before merging—especially because this pipeline would run daily and impact production predictions.

Action:- I ran the branch locally and executed unit tests and a small end-to-end run on a sampled dataset to confirm behavior.- In the PR comments I gave targeted, constructive feedback: - Pointed out a potential race condition in the join logic and suggested a defensive null-check with a small code snippet. - Recommended replacing the custom serializer with our existing utils to avoid duplication and improve observability, citing the existing function and linking to docs. - Asked for a small integration test that asserts schema invariants after the feature-store join. - Suggested a brief design note in the repo describing DAG retry semantics so future engineers understand trade-offs.- When a teammate argued the custom serializer was slightly faster and clearer, I proposed measuring both approaches on a realistic sample and agreed to prioritize maintainability unless performance differences exceeded 10%. We ran a quick benchmark and found no meaningful difference, so we adopted the shared utility.- Later, a senior teammate critiqued my suggestion about the retry semantics as overly conservative. I thanked them, re-evaluated failure modes, and updated the design note to a compromise: two retries with exponential backoff and clearer alerting.

Result: The pipeline merged with additional tests and documentation. Production retrains ran for two weeks with zero failures related to the change. The review improved team conventions (we added the serializer utility to the onboarding docs) and reduced future churn.

Learnings: Be specific and evidence-driven in feedback, offer concrete fixes not just problems, and treat disagreements as experiments—measure when possible and be willing to change your stance when shown better data.

A and B Test DesignMediumTechnical

88 practiced

Design a safe ramp plan for releasing a new pricing page experiment. Define stages with traffic percentages and durations, list primary and guardrail metrics to monitor at each stage, and specify automated and manual rollback criteria. Discuss the trade-offs between learning quickly and minimizing user exposure to risk.

Sample Answer

Approach: staged, metric-driven canary rollout with automated safety gates and manual checkpoints.

Ramp stages:- 0% → 2% (hours: 24) — quick smoke test for stability and instrumentation.- 2% → 10% (days: 3) — early behavior/engagement signals.- 10% → 25% (days: 7) — broader behavioral and revenue signals.- 25% → 50% (days: 7) — operational load and conversion stability.- 50% → 100% (days: 7) — full rollout if stable.

Primary metrics (per stage):- Conversion rate to paid/target action (primary business metric)- Average revenue per user (ARPU) or expected basket value- Click-through and funnel-drop rates on pricing page

Guardrail metrics:- Overall page load time and frontend error rate- Checkout success rate / payment failures- Refund rate or increased support tickets- Net promoter score / qualitative negative feedback spikes- Session abandonment rate

Monitoring cadence & thresholds:- Real-time alerts for severe anomalies (e.g., +5% absolute drop in conversion or +200% payment failures).- Rolling 24h and 7-day comparisons with statistical significance (e.g., p<0.05 or Bayesian credible interval showing >95% probability of harm).

Automated rollback criteria:- Conversion rate drop >10% relative to control with p<0.05 sustained for 4+ hours at ≥10% traffic- Payment failure rate >2x baseline for 30 minutes- Page error rate above SLA (e.g., >1% frontend errors) for 15 minutes

Manual rollback / hold criteria (require on-call + PM review):- Moderate conversion drop (5–10%) sustained 24+ hours- Significant negative user feedback trend or support volume increase (>50% over baseline)- Operational load causing backend degradation (latency or cache thrashing) not resolved within 2 hours

Instrumentation & safeguards:- Feature-flagged rollout with quick toggle- Shadowing/logging full user flows for diagnosing issues- Roll-forward patch path (fix + continue) vs full rollback if critical

Trade-offs:- Faster ramps learn quicker and reduce experiment duration but increase user exposure to potential harm; require stronger automated gates and confidence in metrics.- Slower ramps reduce exposure and allow richer signals (support trends, qualitative feedback) but prolong learning and tie up experiment resources.- Balanced approach: start fast at tiny % for technical safety, then moderate paced increases with stricter statistical checks at higher traffic — this optimizes learning while limiting risk.

Hypothesis Testing and InferenceHardTechnical

30 practiced

Describe how multiple imputation works and how to perform hypothesis testing and compute confidence intervals after multiple imputation. Explain Rubin's rules for pooling parameter estimates and variances across imputed datasets, how to adjust degrees of freedom, and pitfalls when data are not missing at random.

Sample Answer

Multiple imputation (MI) fills in each missing value several times to reflect uncertainty about the true values, analyzes each "completed" dataset with your usual model, then combines results across imputations. Key steps:1. Impute m complete datasets using an appropriate model (e.g., MICE, joint multivariate normal, predictive mean matching) that conditions on variables predictive of missingness and the target.2. Fit the analysis model separately to each dataset to get parameter estimates Q_i and their within-imputation variance estimates U_i (usually the model’s estimated variance-covariance).3. Pool estimates using Rubin’s rules.

Rubin’s rules (notation: i = 1..m):- Q̄ = (1/m) Σ_i Q_i (pooled point estimate)- Ū = (1/m) Σ_i U_i (average within-imputation variance)- B = (1/(m-1)) Σ_i (Q_i − Q̄)(Q_i − Q̄)' (between-imputation variance)- Total variance: T = Ū + (1 + 1/m) B

Inference:- Standard error of Q̄ is sqrt(diag(T)).- Confidence interval for a scalar parameter: Q̄ ± t_{ν,1−α/2} * sqrt(T), where t_{ν} is the Student-t quantile with degrees of freedom ν.- Hypothesis test (e.g., H0: Q = Q0): t = (Q̄ − Q0)/sqrt(T), compare to t_{ν}.

Degrees of freedom (Rubin/Barnard-Rubin approximation):- Relative increase in variance r = (1 + 1/m) * trace(B * Ū^{-1}) / p for multivariate; for scalar parameter use r = (1 + 1/m) B / Ū.- Approximate ν = (m − 1) * (1 + 1/r)^2.For small samples or large r, use the more conservative Barnard-Rubin adjustment that also accounts for finite-sample df from the complete-data analysis.

Practical notes and pitfalls:- The imputation model must be congenial (include predictors and transformations used in analysis) and include auxiliary variables predictive of missingness/outcome to make Missing At Random (MAR) plausible.- If data are Missing Not At Random (MNAR), standard MI assuming MAR can produce biased estimates. Perform sensitivity analyses (pattern‑mixture models, selection models, or varying MNAR parameters), or use targeted MNAR methods.- For non-linear estimands (e.g., quantiles, odds ratios), pool using the same Rubin framework on transformed scale if needed (e.g., pool log-OR then exponentiate), or use bootstrapped MI for better accuracy.- Use enough imputations: m should be >= 20 when missing fraction is moderate/high; higher m reduces Monte Carlo error in B.

This approach yields valid point estimates and standard errors under MAR and properly reflects imputation uncertainty; under MNAR, report sensitivity analyses and transparently state assumptions.

Clean Code and Best PracticesEasyBehavioral

70 practiced

When you review a peer's ML notebook or script, what specific code-quality items do you look for? Provide a checklist focused on maintainability, reproducibility, and correctness (examples: seed control, explicit dtypes, magic numbers, hardcoded paths).

Sample Answer

Situation: As a data scientist, I regularly review peers' notebooks and scripts to ensure models are maintainable, reproducible, and correct.

Task: Provide a concise, actionable checklist I use during reviews.

Action: I check the following items (with brief why/examples):

- Reproducibility - Seed control set and documented (numpy/random/torch/tf) — ensures deterministic runs. - Environment spec (requirements.txt/conda.yml + Python version) or a Dockerfile. - Randomness in data loading/training explicitly controlled (shuffle seeds, workers). - Persistent experiment logging (MLflow/Weights & Biases) and saved artifacts with versioning.

- Maintainability - No hardcoded paths; use relative paths or config files (yaml/env vars). - Clear project structure (data/, src/, notebooks/, models/). - Modular code: functions/classes instead of long linear cells; notebooks with nbdev or scripts for core logic. - Docstrings and inline comments for non-obvious logic; README explaining how to run. - Dependency on external systems isolated behind interfaces.

- Correctness & Safety - Explicit dtypes and units for numeric arrays/dataframes to avoid implicit casts. - Validate assumptions: shape checks, null checks, value ranges, and assertions where appropriate. - No magic numbers — use named constants or config with explanations. - Proper train/validation/test splits with no leakage; cross-validation implemented correctly. - Sanity-check plots and metrics early (label distribution, sample predictions).

- Performance & Testing - Basic unit tests for data transforms and loss/metric calculations. - Efficient data handling (chunking, vectorized ops) and clear complexity notes. - Guardrails for long-running steps (dry-run flags, small-sample mode).

Result / Learning: I give prioritized comments (critical bugs first, then maintainability) and suggest quick fixes or examples. This approach reduces surprises in repro runs and makes the code easier for teammates to extend.

Applying Data Science Techniques to Business ProblemsHardTechnical

68 practiced

You need to estimate the incremental impact of an email re-engagement campaign, but the marketing team used a complex targeting algorithm (no randomization). Describe an offline evaluation strategy that combines observational methods (PSM, doubly robust), sensitivity analyses (Rosenbaum bounds), and a small randomized holdout to triangulate the causal effect. Explain limitations and how you'd allocate budget for the holdout.

Sample Answer

Situation & goal: estimate incremental open/click/purchase lift from a re-engagement email when targeting wasn’t randomized. We need an offline strategy that combines observational causal methods, sensitivity checks, and a small randomized holdout to triangulate credibility.

Strategy (steps):1. Clarify outcome, treatment definition, pre-period covariates, time windows, and sample restrictions (exclude recent engagers).2. Preprocessing & diagnostics: - Check overlap (propensity score distributions) and missingness. - Define covariates strongly predictive of both targeting and outcomes (recency, frequency, monetary, engagement history, product/category).3. Observational estimates: - Propensity Score Matching (PSM): match treated to similar untreated (caliper, nearest neighbor, maybe kernel). Report ATT, balance stats (standardized mean differences). - Doubly Robust estimator: implement IPW + outcome regression (e.g., gradient-boosted trees) to protect against model misspecification; bootstrap CIs.4. Sensitivity analysis: - Rosenbaum bounds to quantify how large an unmeasured confounder’s effect would have to be to change inference. - Also run placebo outcomes (pre-period) and negative controls to detect residual bias.5. Small randomized holdout: - Randomly withhold treatment for a small fraction of the algorithm’s eligible population (pure RCT control). Use this to estimate unbiased ATE for the subpopulation and to assess bias of observational estimators. - Allocation: choose holdout size by power/MDE calculation. If baseline conversion p0 and MDE δ, needed n ≈ 2 * (Z_{1-α/2}+Z_{1-β})^2 * p̄(1-p̄)/δ^2. Practically, 5–10% holdout is common where budget permits; for very low baselines increase holdout (e.g., 10–20%).6. Triangulation & calibration: - Compare observational ATT/ATE to RCT estimate on the holdout subpopulation. If observational methods deviate, estimate bias and consider reweighting/calibrating models or using doubly robust on a model trained to predict discrepancy. - Report a range: observational estimates, RCT estimate (with CIs), and adjusted estimate accounting for sensitivity bounds.

Limitations:- Unmeasured confounding may remain; Rosenbaum gives bounds but not identification.- Small holdout may be underpowered for rare outcomes; trades off revenue/loss vs statistical precision.- Generalizability: holdout subpopulation may differ from full targeting population (use stratified holdouts to improve transportability).- Interference (users receiving multiple emails) and time-varying effects complicate causal interpretation.

Practical recommendations:- Use stratified random holdout (by deciles of propensity or key segments) to improve external validity.- Prioritize covariate collection and pre-period outcomes to strengthen DR estimators.- Present a transparent report: point estimates, sensitivity ranges, and explicit assumptions so stakeholders can weigh business risk when choosing rollout.

Business Impact Measurement and MetricsEasyTechnical

93 practiced

Back-of-the-envelope estimation: An ecommerce site has 1,000,000 unique monthly visitors, current conversion 2%, and average order value $50. The product team believes reducing average checkout time by 10% will lift conversion by 5% relative. Estimate the annual revenue impact of this change and list the assumptions you make.

Data Quality and Edge Case HandlingEasyTechnical

88 practiced

You are given a dataset with several features containing missing values. Explain advantages and disadvantages of these missing-data handling strategies: (a) delete rows with missing values, (b) impute with simple statistics (mean/median/mode), (c) model-based imputation (KNN/MICE), (d) creating an explicit 'missing' category, and (e) leaving as-is with models that support NaNs. In what practical situations would you choose each option? Discuss implications for bias, variance, and downstream analytics.

Sample Answer

(a) Delete rows with missing values- Advantages: Simple, unbiased if data are Missing Completely At Random (MCAR); easy to implement.- Disadvantages: Loss of data → higher variance, reduced power; biased if Missing At Random (MAR) or Missing Not At Random (MNAR).- When: Small fraction of missingness (<5–10%) and MCAR, or for quick baseline models.

(b) Impute with simple statistics (mean/median/mode)- Advantages: Fast, preserves sample size, reasonable for numeric features with near-symmetric distributions (mean) or skewed (median).- Disadvantages: Underestimates variance (shrinks spread), can bias relationships and attenuate correlations → biased parameter estimates, lower predictive uncertainty.- When: Exploratory analysis, pipelines where simplicity and speed matter, or features with few missing and weak predictive importance.

(c) Model-based imputation (KNN/MICE)- Advantages: Uses multivariate patterns to produce more realistic values; preserves relationships between variables; reduces bias vs simple imputation.- Disadvantages: More compute, risk of overfitting or leakage if not done within CV folds; assumptions about imputation model; may inflate variance if multiple imputations not combined.- When: Substantial missingness, MAR plausible, downstream inference or production models where preserving joint distributions matters. Use MICE for inference, multiple imputations to capture uncertainty.

(d) Explicit 'missing' category- Advantages: For categorical variables, preserves missingness as informative signal (MNAR); model can learn missingness as feature.- Disadvantages: Can create spurious categories, complicate interpretation; not applicable to numeric without binning.- When: Missingness itself may be predictive (e.g., no-response indicates status), or for tree-based models that handle categories well.

(e) Leave as-is with models that support NaNs- Advantages: Minimal preprocessing; some algorithms (XGBoost, LightGBM) have native handling and can exploit missingness.- Disadvantages: Behavior depends on model; might implicitly route missing values and obscure analysis; not suitable if you need interpretable imputations.- When: Using tree ensembles in production and missingness is common; for quick prototyping.

Implications summary:- Bias: Deletion and simple imputation can introduce bias under MAR/MNAR. Model-based and explicit-missing can reduce bias if assumptions hold.- Variance: Deletion increases variance; deterministic imputations reduce apparent variance; multiple imputation captures uncertainty.- Downstream analytics: For inference, prefer multiple imputation/MICE to get valid standard errors. For prediction, model-based or native-NaN handling often yields best performance—always validate with proper CV and avoid leakage.

Practice Data Scientist questions across all topics

Additional Information

Want to create your own tailored preparation guide using our deep research?

Get Started for Free

Interview-Ready Courses

Visual-first, interactive, structured learning paths

Browse Data Scientist jobs

AI-enriched listings across hundreds of company career pages

Explore Jobs

Entry Level Data Scientist Interview Preparation Guide - FAANG Standards

Interview Process Overview

Interview Rounds

Recruiter Screening

What to Expect

Tips & Advice

Focus Topics

Handling Questions About Gaps or Weaknesses

Practice Interview

Study Questions

Motivation and Culture Fit

Practice Interview

Study Questions

Background and Experience Communication

Practice Interview

Study Questions

Technical Proficiency Confirmation

Practice Interview

Study Questions

Technical Phone Screen

What to Expect

Tips & Advice

Focus Topics

Code Efficiency and Readability

Practice Interview

Study Questions

Edge Cases and Data Quality

Practice Interview

Study Questions

Problem-Solving Process and Communication

Practice Interview

Study Questions

Python Data Manipulation with Pandas and NumPy

Practice Interview

Study Questions

SQL Fundamentals

Practice Interview

Study Questions

Coding Interview - Data Manipulation and SQL

What to Expect

Tips & Advice

Focus Topics

Problem Analysis and Approach Selection

Practice Interview

Study Questions

Handling Data Quality Issues

Practice Interview

Study Questions

Metric Calculation and Aggregation

Practice Interview

Study Questions

Advanced SQL Techniques

Practice Interview

Study Questions

Data Joining and Merging Strategies

Practice Interview

Study Questions

Statistics and Experimentation Interview

What to Expect

Tips & Advice

Focus Topics

Interpreting and Communicating Results

Practice Interview

Study Questions

Experiment Design and Practical Considerations

Practice Interview

Study Questions

Common Statistical Distributions and Concepts

Practice Interview

Study Questions

A/B Testing Fundamentals

Practice Interview

Study Questions

Hypothesis Testing and Statistical Significance

Practice Interview

Study Questions

Product Sense and Case Study Interview

What to Expect

Tips & Advice

Focus Topics