Data Scientist Interview Questions

Showing 691–720 of 1076 results

• Morgan StanleyEasy·Data Scientist
  Compute win probability in coin-toss game

  This question evaluates understanding of probability theory and stochastic processes, focusing on win probability in an alternating coin-toss game and reasoning about pattern occurrence and stopping times in random sequences.

  Coding & AlgorithmsTechnical Screen
• Morgan StanleyMedium·Data Scientist
  Estimate Greeks and hedging PnL

  This question evaluates understanding of option Greeks (delta, gamma, vega), mental approximation of Gaussian probabilities, and the ability to reason about delta-hedged profit-and-loss under Black-Scholes model misspecification, categorized under Software Engineering Fundamentals for a Data Scientist role.

  Software Engineering FundamentalsTechnical Screen
• Morgan StanleyEasy·Data Scientist
  Explain forward contracts and options

  This question evaluates understanding of financial derivatives—specifically forwards and options—assessing competency in financial instruments, contractual rights and obligations, pricing concepts, and how these concepts inform data-driven modeling for a Data Scientist role.

  Software Engineering FundamentalsTechnical Screen
• MMorningstarEasy·Data Scientist
  Explain a stochastic process simply

  This question assesses the candidate's ability to intuitively define a stochastic process, differentiate it from static probability models, and articulate fundamental properties like stationarity and the Markov property.

  Statistics & MathTechnical ScreenStatisticsStochastic ProcessesTime Series
• NextdoorMedium·Data Scientist
  Measure Local News Launch Success

  This problem tests your ability to design an experimentation framework for a localized product launch and identify potential confounding factors and interference effects.

  Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMetrics
• NotionMedium·Data Scientist
  Analyze Document Collaboration CSVs

  This problem tests your ability to clean, join, and analyze messy user-activity data using pandas or SQL, focusing on window functions and handling missing values.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasData Cleaning
• NotionEasy·Data Scientist
  Analyze document collaboration from CSV logs

  This question evaluates data manipulation and analytical competencies—particularly deduplication, joins, aggregation, co-occurrence counting, and metric definition—applied to CSV access logs and user metadata.

  Data Manipulation (SQL/Python)Technical Screen
• NotionMedium·Data Scientist
  Analyze document collaboration patterns

  This question evaluates a candidate's competency in data manipulation and analytics—specifically joins, deduplication of user-document pairs, aggregation, and computation of user- and country-level collaboration metrics using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• OneMain FinancialMedium·Data Scientist
  Calculate Break-Even Point and Profit Impact Analysis

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Break-Even Point and Profit Impact Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• OneMain FinancialEasy·Data Scientist
  Calculate Break-even for New Credit Card Product Launch

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Break-even for New Credit Card Product Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• OneMain FinancialMedium·Data Scientist
  Determine Channel Performance with Additional Metrics Needed

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Channel Performance with Additional Metrics Needed states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• OneMain FinancialMedium·Data Scientist
  Differentiate and control Type I/II errors

  This question evaluates understanding of hypothesis testing fundamentals such as Type I/Type II errors, statistical power and sample size calculation for two-proportion tests, family-wise error rate control (e.g., Bonferroni), and sequential analysis/alpha-spending schemes.

  Statistics & MathTechnical Screen
• OneMain FinancialEasy·Data Scientist
  Evaluate shift from branch to digital channel

  This question evaluates a data scientist's ability to analyze channel unit economics, design growth experiments and metrics, and assess operational, fraud, and credit risk implications for shifting acquisition from branch to digital in a credit-card business.

  Analytics & ExperimentationTechnical Screen
• OneMain FinancialEasy·Data Scientist
  Explain decision trees and tree ensembles

  This question evaluates understanding of decision trees, splitting criteria for classification versus regression, hyperparameter effects on the bias–variance trade-off, and the mechanics and rationale behind tree-based ensemble methods in the Machine Learning domain.

  Machine LearningTechnical Screen
• OneMain FinancialMedium·Data Scientist
  Maximize Probability of Drawing Two Red Balls

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Maximize Probability of Drawing Two Red Balls states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• OneMain FinancialMedium·Data Scientist
  Maximize credit card portfolio profit

  This problem tests your ability to model expected profitability by balancing credit risk (probability of default) against revenue generation and defining optimal decision thresholds.

  Analytics & ExperimentationTechnical ScreenCredit RiskProfit ModelingDecision Strategy
• OneMain FinancialMedium·Data Scientist
  Optimize SaaS pricing and profit

  This question evaluates pricing optimization, linear demand modeling, capacity-constrained profit maximization, and the impact of subscription churn on lifetime value within an Analytics & Experimentation data-science context.

  Analytics & ExperimentationTechnical Screen
• OneMain FinancialHard·Data Scientist
  Present a project to non-technical leaders

  This question evaluates a data scientist's ability to communicate a modeling project's business framing, metric choices, trade-offs, risk mitigation, and quantified impact while handling cross-functional questions.

  Behavioral & LeadershipTechnical Screen
• OneMain FinancialHard·Data Scientist
  Select and tune XGBoost hyperparameters

  This question evaluates skills in selecting and tuning XGBoost hyperparameters, managing severe class imbalance and sparse one‑hot encodings, handling missing values, and designing compute‑efficient training and grouped cross‑validation to prevent user‑level leakage.

  Machine LearningTechnical Screen
• OneMain FinancialEasy·Data Scientist
  Walk through a DS project end-to-end

  This question evaluates end-to-end data science competencies including problem framing, metric design, data sourcing and quality assessment, modeling and evaluation choices, risk identification, impact quantification, and stakeholder communication within the Behavioral & Leadership category for a Data Scientist position.

  Behavioral & LeadershipTechnical Screen
• OpenAIHard·Data Scientist
  Assess free-month promotion impact

  This question tests your ability to design a rigorous experimentation framework to measure the causal impact of a promotional offer while accounting for self-selection bias and long-term churn behavior.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceProduct Analytics
• OpenAIHard·Data Scientist
  Compute churn with re-subscriptions

  This problem tests your ability to reconstruct user subscription states over time from raw event logs, specifically identifying active status to calculate monthly churn rates.

  Data Manipulation (SQL/Python)Technical ScreenSQLData ManipulationTime-series
• OpenAIEasy·Data Scientist
  Compute signup rate and retention from raw logs

  This question evaluates the ability to manipulate event-level logs to compute cohort metrics—specifically signup rate and D30 retention—testing proficiency in SQL/Python, event-time windowing, joins, aggregation, and understanding of upstream logging semantics.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Debug and harden trial-assignment Python code

  This question evaluates understanding of experiment assignment, data integrity, reliability engineering, concurrency, observability, and privacy in data systems, testing competencies around randomness, idempotency, time semantics, retries, and failure-mode identification.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIHard·Data Scientist
  Design a free-month experiment

  This question evaluates experimental design, causal inference, metric selection, bias mitigation, ROI estimation, and stakeholder communication skills within the Analytics & Experimentation domain for a Data Scientist role.

  Analytics & ExperimentationTechnical Screen
• OpenAIEasy·Data Scientist
  Evaluate a free-trial A/B test

  This question tests your ability to design an A/B test for a freemium product, interpret statistical results, and address potential biases like cannibalization or selection effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsRetention
• OpenAIHard·Data Scientist
  Handle repeated churn in SQL

  This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsTime Series
• OpenAIMedium·Data Scientist
  How would you evaluate a free-trial A/B test?

  Evaluates skills in A/B test design and causal inference within the Analytics & Experimentation domain, emphasizing precise metric definition (including guardrails), choice of analysis population (ITT vs per‑protocol), handling of post‑treatment selection, and interpretation of binary conversion and retention metrics; the abstraction level is technical and oriented toward intermediate-to-senior data scientists who must combine statistical rigor with product impact thinking. Commonly asked because it tests the ability to balance acquisition versus downstream retention and revenue, to identify typical analysis pitfalls (time-window errors, incorrect joins/denominators, leakage, peeking, and selection bias), and to translate experiment results into a business recommendation linked to value metrics like LTV and payback period.

  Analytics & ExperimentationTechnical Screen
• OpenAIHard·Data Scientist
  Measure free-month promotion impact

  This question tests the ability to design an experiment for a subscription product, focusing on causal inference, handling right-censoring in cohort data, and calculating the Return on Investment (ROI) for a promotional offer.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperiment DesignROI
• OpenAIHard·Data Scientist
  Write SQL for repeat churn

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  Data Manipulation (SQL/Python)Technical Screen