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Interview questions
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Interview questions · CVS Health
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.
Interview questions · Meta
Compute ad revenue metrics by geography in SQL
In the Data Manipulation (SQL/Python) domain, this intermediate-level question evaluates SQL aggregation, join logic between impressions and clicks, date-based UTC attribution, user-geo joins, and null/divide-by-zero-safe computation of derived metrics (CTR, RPM) at the country-day granularity.
Interview questions · Capital One
Compute and interpret 95% confidence intervals
This question evaluates proficiency with confidence intervals, hypothesis testing for proportions and means, multiple-comparison adjustment (Bonferroni), and sample size calculations, including assessment of normal-approximation versus Wilson score methods.
Interview questions · Capital One
Compute credit-card portfolio profit and breakeven
This question evaluates financial modeling, unit-economics calculation, breakeven analysis, and basic risk/expected-loss estimation relevant to a Data Scientist, testing competency in statistics & math and algebraic derivation within financial analysis.
Interview questions · Coinbase
Compute delivery metrics and top-K queries
Compute delivery metrics and top-K queries evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Akuna Capital
Compute delivery order via BFS
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute delivery order via BFS states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Capital One
Compute energy needed for 10% ROI
This question evaluates financial modeling and quantitative analysis skills, including ROI calculation, fixed and variable cost accounting, capacity utilization, break-even and sensitivity analysis in an energy project context for a data scientist role.
Interview questions · Meta
Compute feed ad frequency and retention in SQL
This question evaluates SQL-based product analytics skills, specifically session-level aggregation for ad frequency, impression-to-click joins for CTR by feed position bins, cohort-based retention computation, and user-fixed-effect adjustments; it is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.
Interview questions · DoorDash
Compute Fitness App DAU
This question evaluates skills in data manipulation and time-series analytics, including SQL joins, deduplication of non-test users, UTC date bucketing, aggregation for daily active users (DAU), and computation of rolling averages.
Interview questions · Capital One
Compute gala vs online break-even donors
This question evaluates a candidate's skill in expected-value computation, probability-weighted revenue modeling, break-even analysis, and sensitivity analysis of cost parameters within a donor-segmentation context.
Interview questions · Meta
Compute Heavy-Caller Percentages
This question evaluates proficiency in time-windowed data aggregation, user-level counting and percentage calculations using SQL or Python for analytics tasks.
Interview questions · Instacart
Compute immediate delivery percentage
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute immediate delivery percentage states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Meta
Compute interval mode, BST range sum, exclusive time
This multipart question evaluates proficiency in interval coverage and frequency analysis, binary search tree range-sum queries with preprocessing for repeated queries, and exclusive-time computation from call logs, covering concepts such as event counting/sweep-line, BST traversal and augmentation, and nested-call time accounting.
Interview questions
Compute p-values and adjust multiplicity
This question evaluates proficiency in statistical inference, covering exact and approximate hypothesis testing for binomial outcomes, normal approximation with continuity correction, multiple-testing adjustments (Bonferroni and Holm), interpretation of p-values, and decision rules for choosing z- versus t-tests, all within the Statistics & Math domain. It is commonly asked to assess understanding of exact versus approximate methods, control of familywise error, and conceptual distinctions about what p-values represent, testing both practical application and conceptual understanding of statistical assumptions and procedures.
Interview questions · Amazon
Compute p-values, CIs, and adjust multiples
This question evaluates a candidate's proficiency in statistical inference, covering hypothesis testing and test selection, confidence interval construction, multiple-testing corrections, error types, and asymptotic reasoning.
Interview questions · Lyft
Compute Poisson supply–demand match probability
This question evaluates understanding of probability distributions and stochastic modeling—specifically Poisson processes, the Skellam distribution for differences, expectation of minima, and probabilistic capacity planning within the Statistics & Math domain.
Interview questions · Snapchat
Compute posterior spam risk from flags
This question evaluates understanding of Bayes' theorem, conditional probability, and interpretation of classifier performance metrics (true positive and false positive rates) for computing posterior spam risk.
Interview questions · Gemini
Compute power and cost-aware thresholds
This question evaluates a data scientist's competency in statistical estimation, experimental design, cost-sensitive loss modeling, and sequential testing as applied to fraud-detection rules, encompassing prevalence estimation, expected-value cost calculations, sample-size/power derivation, confidence-interval construction, and alpha-controlled monitoring. It is commonly asked in Statistics & Math interviews because it probes practical application of probabilistic modeling and A/B testing under operational constraints, emphasizing practical application-level understanding rather than purely conceptual theory.
Interview questions · TikTok
Compute rooms and verify tree completeness
This question evaluates understanding of interval overlap and resource allocation for meeting-room scheduling alongside recognition of binary tree structure and completeness properties.
Interview questions · Flatiron Health
Compute sample size and significance
This question evaluates understanding of statistical power, two-proportion sample-size calculation, hypothesis testing, and variance-reduction techniques (CUPED) in the context of binary A/B experiments.
Interview questions · Meta
Compute sample size and test duration
This question evaluates competency in experimental design and statistical inference for A/B testing, including sample size and power calculations, multiple-comparison correction, hypothesis-test selection (z vs t), confidence-interval interpretation, and randomization/balance diagnostics.
Interview questions · DoorDash
Compute sample sizes and error control
This question evaluates proficiency in experimental design and applied inferential statistics—specifically sample size calculation for means and proportions, non-inferiority testing, multiple-comparison error control, cluster-randomized design effects, and sequential monitoring boundaries—within the Statistics & Math domain for a Data Scientist role. It is commonly asked to measure the ability to apply statistical formulas and error-control principles under practical constraints, balancing power and type I error while accounting for clustering and interim looks; the assessment emphasizes practical application grounded in conceptual understanding of inferential methods.
Interview questions · Meta
Compute seller counts and vehicle share
This question evaluates data manipulation and analytical SQL competencies such as joining tables, aggregating and deduplicating interaction counts, applying date-window filters, and computing category-level percentage metrics.
Interview questions · CVS Health
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.