Data Scientist Interview Questions

Showing 301–330 of 1971 results

• C3 AIHard·Data Scientist
  Forecast 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 AIHard·Data Scientist
  Give Evidence-Based Strengths, Weaknesses, and Feedback

  This question tests the candidate's self-awareness, professional maturity, and ability to translate past critiques into tangible behavioral growth.

  Behavioral & LeadershipOnsiteLeadershipSelf-AwarenessCommunication
• C3 AIMedium·Data Scientist
  Identify What Does Not Fix Vanishing Gradients

  Explain why raising the learning rate does not fix vanishing gradients and compare skip connections, ReLU, and batch normalization.

  Machine LearningOnline Assessment
• C3 AIEasy·Data Scientist
  Identify 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 AIHard·Data Scientist
  Keep 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 AIMedium·Data Scientist
  Longest 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
• C3 AIHard·Data Scientist
  Reduce Fresh-Food Waste with an End-to-End ML Approach

  This problem tests the ability to design an integrated ML system that moves beyond simple demand forecasting by addressing inventory dynamics, supply constraints, and decision optimization.

  ML System DesignOnsiteSupply ChainOptimizationCensored Data
• CVS HealthMedium·Data Scientist
  Aggregate 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 HealthMedium·Data Scientist
  Build a leak-free sklearn churn pipeline

  This question evaluates practical competencies in building a reproducible scikit-learn churn prediction pipeline—covering temporal splitting to avoid leakage, preprocessing, calibration, hyperparameter tuning, evaluation with ROC AUC/PR AUC and F1-based thresholds, and permutation feature importance—and is in the Machine Learning domain.

  Machine LearningOnline Assessment
• CVS HealthMedium·Data Scientist
  Calculate 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 HealthMedium·Data Scientist
  Compute A/B significance, CI, and power

  This question evaluates proficiency in statistical inference for A/B testing—specifically two-proportion z-tests, construction of confidence intervals with unpooled standard errors, sample-size and power calculations, and multiple-comparison correction via Bonferroni.

  Statistics & MathOnline Assessment
• CVS HealthEasy·Data Scientist
  Compute 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 HealthMedium·Data Scientist
  Create Views, Insert, and Update Correctly

  This question evaluates SQL data-manipulation competencies including view creation, aggregate joins, INSERT/UPDATE operations, and reasoning about temporal effects on computed aggregates such as historical customer spend.

  Data Manipulation (SQL/Python)Online Assessment
• CVS HealthMedium·Data Scientist
  Create and query an e-commerce schema

  This question evaluates SQL schema design and data manipulation competencies, including defining PostgreSQL table types and constraints, inserting exact datasets, composing aggregate and join queries with date-based filters, and performing DML updates with verification.

  Data Manipulation (SQL/Python)Online Assessment
• CVS HealthHard·Data Scientist
  Design 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 HealthHard·Data Scientist
  Design 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 HealthMedium·Data Scientist
  Explain 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 HealthHard·Data Scientist
  Handle 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
• CVS HealthMedium·Data Scientist
  Implement R² and Compare PCA With/Without Scaling

  This question evaluates a candidate's proficiency with regression evaluation metrics and linear-algebra-based dimensionality reduction, specifically implementing a numerically robust R² scorer and performing PCA with and without feature standardization using NumPy.

  Machine LearningOnline Assessment
• CVS HealthHard·Data Scientist
  Launch and measure a TV campaign

  This question evaluates a data scientist's competency in experimental design and causal inference, including geo-level matching, KPI definition, media measurement (GRP/TRP, reach/frequency, adstock and saturation modeling), power analysis, and operational risk management for measuring incremental vaccinations.

  Analytics & ExperimentationTechnical Screen
• CVS HealthHard·Data Scientist
  Lead structured response to accuracy incident

  This question evaluates incident leadership, operational decision-making, and data-driven troubleshooting skills for a data scientist, focusing on triage, hypothesis formation, mitigation choice, cross-functional communication, and recurrence prevention.

  Behavioral & LeadershipTechnical Screen
• CVS HealthEasy·Data Scientist
  Reduce LLM hallucination and handle class imbalance

  This question tests your ability to design a robust pipeline for detecting clinical inaccuracies in LLM outputs while managing highly imbalanced datasets where missing a critical diagnosis is costlier than a false alarm.

  Machine LearningTechnical ScreenLLMRAGImbalanced Learning
• CVS HealthMedium·Data Scientist
  Tune classifier and compute key metrics

  This question tests your ability to optimize a classification threshold based on business costs and your understanding of how to bridge classification and regression tasks.

  Machine LearningTechnical ScreenClassificationCost-sensitive LearningThresholding
• CVS HealthMedium·Data Scientist
  Use pandas to aggregate, pivot, and label

  This question evaluates competency in pandas-based tabular data manipulation—specifically DataFrame merging, groupby aggregation, pivot table construction, counting unique values, and conditional flagging with numpy—within the domain of Data Manipulation (SQL/Python) and is primarily at the practical application level.

  Data Manipulation (SQL/Python)Technical Screen
• CVS HealthMedium·Data Scientist
  Write SQL for dedup and purchase shares

  This question evaluates proficiency in SQL data manipulation skills including entity deduplication, canonicalization rules, window functions, common table expressions, joins, and aggregation to derive per-user purchase counts and distributions.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneHard·Data Scientist
  Address face recognition concerns with stakeholders

  This question evaluates competency in risk identification and prioritization, privacy and regulatory compliance, model fairness and governance, technical verification metrics such as liveness and accuracy, and stakeholder management for biometric system deployment.

  Behavioral & LeadershipOnsite
• Capital OneMedium·Data Scientist
  Aggregate exam scores with NULL handling

  This question evaluates proficiency in SQL data manipulation, specifically joins (including LEFT JOIN behavior), NULL handling with ISNULL/COALESCE, conditional aggregation, distinct counts, and date-based filtering within the Data Manipulation (SQL/Python) domain, focusing on practical application of query-writing skills.

  Data Manipulation (SQL/Python)Online Assessment
• Capital OneMedium·Data Scientist
  Analyze Subscription, Insurance, App, and Card Cases

  This question evaluates product analytics competencies—unit-economics, cohort churn modeling, LTV/CAC and funnel math—requiring clear assumptions, metric definitions, governing formulas, and numeric break-even and conversion calculations.

  Analytics & ExperimentationOnline Assessment
• Capital OneMedium·Data Scientist
  Analyze ad watch-time with Excel pivots

  This question evaluates a data scientist's proficiency with Excel PivotTables, date and ISO-week handling, aggregation and ratio metrics (clicks per watch-minute), and deterministic tie-breaking logic for reproducible results.

  Analytics & ExperimentationTechnical Screen
• Capital OneMedium·Data Scientist
  Analyze your favorite app and improve it

  This question evaluates a data scientist's competencies in product analytics, monetization analysis, competitive positioning, metric specification (north-star and guardrails), and experimental design including power and validity considerations.

  Analytics & ExperimentationOnsite