CVS Health interview questions

Showing 1–18 of 18 results

• Aggregate radiology spend and derive fiscal monthMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Build a leak-free sklearn churn pipelineMedium

  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.

  CVS HealthData ScientistMachine LearningOnline Assessment
• Calculate annual percentages and YoY by cohortsMedium

  This question evaluates SQL and pandas data manipulation skills, specifically joins, conditional aggregation, deduplication, cohort-based percentage computations, and year-over-year change calculations.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute A/B significance, CI, and powerMedium

  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.

  CVS HealthData ScientistStatistics & MathOnline Assessment
• Compute specialty spend share and top age bandEasy

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Create Views, Insert, and Update CorrectlyMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Online Assessment
• Create and query an e-commerce schemaMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Online Assessment
• Design a flu-shot A/B/n campaign experimentHard

  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.

  CVS HealthData ScientistAnalytics & ExperimentationTechnical Screen
• Design classification under missingness and imbalanceHard

  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.

  CVS HealthData ScientistMachine LearningTechnical Screen
• Explain p-value and choose correct testMedium

  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.

  CVS HealthData ScientistStatistics & MathTechnical Screen
• Handle challenges in MMM/MMXHard

  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.

  CVS HealthData ScientistMachine LearningTechnical Screen
• Implement R² and Compare PCA With/Without ScalingMedium

  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.

  CVS HealthData ScientistMachine LearningOnline Assessment
• Launch and measure a TV campaignHard

  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.

  CVS HealthData ScientistAnalytics & ExperimentationTechnical Screen
• Lead structured response to accuracy incidentHard

  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.

  CVS HealthData ScientistBehavioral & LeadershipTechnical Screen
• Reduce LLM hallucination and handle class imbalanceEasy

  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.

  CVS HealthData ScientistMachine LearningTechnical Screen
• Tune classifier and compute key metricsMedium

  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.

  CVS HealthData ScientistMachine LearningTechnical Screen
• Use pandas to aggregate, pivot, and labelMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for dedup and purchase sharesMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen