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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 151–180 of 1076 results
- ByteDanceMediumData ScientistCompute Minimum Parentheses Additions
This question evaluates proficiency in string processing and algorithmic reasoning for balancing parentheses, testing understanding of invariants and edge-case handling.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumData ScientistCount Same-Day Registered Posters
This question evaluates SQL querying skills, including joining relational tables, date handling, aggregation, and deduplicating users across events. Commonly asked in Coding & Algorithms interviews for data scientists, it assesses practical relational database querying and analytics tasks—filtering, grouping, and counting distinct users across tables—and tests practical application of SQL rather than purely conceptual database theory; this summary is in English.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumData ScientistDetermine Whether an Integer Is a Palindrome
This problem tests your ability to manipulate integers mathematically to detect palindromic sequences without converting the number into a string, requiring careful handling of edge cases and integer overflow.
Coding & AlgorithmsTechnical ScreenMathAlgorithmsInteger Manipulation - ByteDanceMediumData ScientistDiagnose Low CTR in an Advertising Campaign Funnel
Prepare for a product analytics interview by diagnosing below-average advertising CTR from limited campaign-level funnel metrics. The question tests metric interpretation, uncertainty, evidence-versus-hypothesis discipline, and how to plan useful follow-up analysis.
Analytics & ExperimentationTechnical Screen - ByteDanceMediumData ScientistDiscuss Your Data Analytics and Causal-Inference Experience
Practice describing data analytics and causal-inference experience while separating descriptive insights from credible intervention effects.
Behavioral & LeadershipTechnical Screen - ByteDanceMediumData ScientistExplain Type I/II errors vs precision/recall
This question evaluates understanding of Type I and Type II errors in hypothesis testing and binary classification, their correspondence to false positives and false negatives, and how those concepts relate to precision and recall within the Statistics & Math domain.
Statistics & MathTechnical Screen - ByteDanceMediumData ScientistFind Each User's Earliest Seven-Day Activity Streak
Tests your ability to identify consecutive date sequences (gaps-and-islands problem) in PostgreSQL, aggregate streaks, and filter for each user's earliest qualifying period.
Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions - ByteDanceMediumData ScientistFind top-paid employee per department
This problem tests your ability to perform group-wise filtering and ranking using window functions or joins to extract top-performing records within categories.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation - ByteDanceMediumData ScientistHow would you choose a classification threshold?
This question evaluates understanding of threshold selection for probabilistic binary classifiers, testing competencies in validation-set decision threshold choice, cost-sensitive trade-offs between false positives and false negatives, effects of class imbalance, and probability calibration.
Machine LearningTechnical Screen - ByteDanceHardData ScientistInterpret and validate regression with interactions
This question evaluates understanding of regression modeling with interaction terms, binary outcome models and odds/marginal-effect interpretation, causal inference concepts like endogeneity and instrumental variables, model diagnostics and standard-error choices, and count-data modeling.
Statistics & MathTechnical Screen - ByteDanceHardData ScientistPredict Short-Video Engagement with Skewed Data
Design a short-video engagement model with explicit targets, leakage-safe features, heavy-tail handling, exposure awareness, and temporal evaluation.
Machine LearningTechnical Screen - ByteDanceMediumData ScientistProbability That One Fair Die Beats Another
Solve a coding interview problem about the probability that one fair die beats another when the dice may have different sizes and duplicate face values. The challenge emphasizes strict tie handling and an efficient approach that avoids comparing every possible pair of faces.
Coding & AlgorithmsTechnical Screen - C3 AIMediumData ScientistChoose Tokenization and Dense or Sparse Retrieval
This problem tests your understanding of document retrieval strategies, specifically comparing sparse and dense representations and how to integrate them into a production RAG system.
Machine LearningTechnical ScreenRAGNLPInformation Retrieval - C3 AIHardData ScientistCompare PCA and Autoencoders Under the Curse of Dimensionality
This question tests your conceptual understanding of dimensionality reduction techniques and your ability to weigh their performance in high-dimensional feature spaces prone to the curse of dimensionality.
Machine LearningTechnical ScreenDimensionality ReductionPCAAutoencoders - C3 AIHardData ScientistCompare Transformers and RNNs and Derive Attention Complexity
Evaluate candidate's deep learning foundations by comparing Transformer and recurrent architectures and deriving exact time/memory complexities for standard scaled dot-product attention across training and inference.
Machine LearningTechnical ScreenTransformersRNNAttention Mechanism - C3 AIHardData ScientistConnect Bias-Variance Trade-offs to Bagging and Boosting
This question tests the conceptual understanding of how ensemble methods manipulate model error components through different structural paradigms.
Machine LearningTechnical ScreenBias-VarianceEnsemble LearningBagging - C3 AIHardData ScientistEvaluate a Retrieval-Augmented Generation System
Design an end-to-end evaluation framework for an enterprise RAG system that assesses retrieval quality, generative fidelity, claim-level attribution, security controls, and production telemetry.
ML System DesignTechnical ScreenRAGLLM EvaluationSystem Design - C3 AIMediumData ScientistExplain the Gaussian Distribution and Its Limits
Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.
Statistics & MathTechnical ScreenStatisticsProbabilityAnomaly Detection - C3 AIHardData ScientistFind longest substring with at least k repeats
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find longest substring with at least k repeats states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - C3 AIHardData ScientistForecast Food Stocking Needs Under Waste and Stockout Costs
Design an end-to-end inventory forecasting and stocking optimization system for perishable food items under asymmetric costs of spoilage versus stockouts. The design covers handling censored sales data, probabilistic demand modeling, cost-optimal stocking policies, and validation strategy.
ML System DesignTechnical Screenml-system-designtime-seriessupply-chain - C3 AIEasyData ScientistIdentify a classic bagging algorithm
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Identify a classic bagging algorithm states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - C3 AIHardData ScientistKeep Gradient Descent from Stalling at Poor Solutions
Design a rigorous diagnostic procedure and remediation plan for gradient descent stalling at suboptimal loss values, differentiating true local minima from saddle points, vanishing gradients, and poor conditioning.
Machine LearningTechnical ScreenOptimizationDeep LearningLoss Landscapes - C3 AIMediumData ScientistLongest Substring Where Every Character Appears at Least K Times
Find the longest substring in which every present character appears at least k times. Practice divide-and-conquer splitting, frequency reasoning, and fixed-alphabet complexity.
Coding & AlgorithmsTechnical Screen - CVS HealthMediumData ScientistAggregate radiology spend and derive fiscal month
This question evaluates a candidate's competency in data manipulation and preprocessing using SQL/Python, covering robust dtype specification, null handling, numeric aggregation and percentage computation, datetime parsing, and fiscal-month derivation.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthMediumData ScientistCalculate annual percentages and YoY by cohorts
This question evaluates SQL and pandas data manipulation skills, specifically joins, conditional aggregation, deduplication, cohort-based percentage computations, and year-over-year change calculations.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthEasyData ScientistCompute specialty spend share and top age band
This question evaluates data manipulation and aggregation skills in SQL and Pandas, including joins/unions across disjoint member tables, computation of specialty spend shares, identification of top age bands by claim counts with tie handling, year-based date filtering, and categorical recoding with monthly grouping.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthHardData ScientistDesign a flu-shot A/B/n campaign experiment
This question evaluates a data scientist's competency in experimental design, causal inference, sample-size and power calculations, compliance-adjusted effect estimation, attribution instrumentation, and reporting for multi-channel marketing experiments.
Analytics & ExperimentationTechnical Screen - CVS HealthHardData ScientistDesign classification under missingness and imbalance
This question evaluates a data scientist's competency in missing data diagnosis and imputation, handling severe class imbalance, choosing and interpreting classification and calibration metrics, and improving logistic regression as part of an end-to-end predictive pipeline on claims and EHR data, and it falls under the Machine Learning domain.
Machine LearningTechnical Screen - CVS HealthMediumData ScientistExplain p-value and choose correct test
This question evaluates a candidate's understanding of statistical inference, hypothesis testing, and robust test selection, focusing on p-value interpretation and choosing between paired vs independent and parametric vs rank-based tests.
Statistics & MathTechnical Screen - CVS HealthHardData ScientistHandle challenges in MMM/MMX
This question evaluates a data scientist's competency in applied Machine Learning and causal inference for marketing-mix modeling, focusing on diagnosing model fragility from multicollinearity, endogeneity, non‑stationarity, measurement error, and decisions around adstock, saturation and promotion effects.
Machine LearningTechnical Screen
C3 AI
CVS Health