Data Scientist Interview Questions

Showing 1171–1200 of 1971 results

• MetaMedium·Data Scientist
  Model Unique Recipients with Poisson Distribution and Test Fit

  Evaluates statistical modeling of unique recipient counts per caller using count distributions. Strong answers compare Poisson, binomial, negative binomial, zero-inflated, and segmented models, then test fit with dispersion checks, likelihood metrics, chi-square tests, and simulation-based diagnostics.

  Statistics & MathTechnical Screen
• MetaMedium·Data Scientist
  Model session times and comments with exponential/Poisson

  This question evaluates understanding of memoryless processes and count-data modeling, covering exponential (constant-hazard) distributions for session durations and Poisson approximations for comment counts within survival analysis and count-data modeling in Statistics & Math for a Data Scientist role.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Optimize Oculus Data Streaming with Bandwidth Constraints

  This question evaluates a candidate's ability in algorithm design and problem decomposition for array segmentation and interval coverage, focusing on reasoning about cumulative constraints for streaming data and geometric interval intersections.

  Coding & AlgorithmsOnsite
• MetaMedium·Data Scientist
  Optimize Travel Costs and Generate Rotational Symmetric Numbers

  This question evaluates algorithmic optimization and complexity-analysis skills by combining array-based time-dependent pricing for round-trip cost minimization with combinatorial generation of rotationally symmetric (strobogrammatic) k-digit numbers.

  Coding & AlgorithmsTechnical Screen
• MetaHard·Data Scientist
  Persuade engineers to launch pinned-unread chats

  This question evaluates persuasion, product sense, metrics-driven reasoning, and technical trade-off analysis for a Data Scientist within a Behavioral & Leadership domain, requiring clear problem definition, baseline metrics, impact estimation, and consideration of engineering constraints like ranking complexity, real-time updates, caching, client performance, edge cases, and accessibility. It is commonly asked to assess the ability to influence engineering stakeholders, design incremental rollouts with success metrics and guardrails, and balance high-level conceptual strategy with practical implementation planning (both conceptual understanding and practical application).

  Behavioral & LeadershipTechnical Screen
• MetaMedium·Data Scientist
  Promote Inclusion and Overcome Barriers in Social-Commerce Team

  Promote Inclusion and Overcome Barriers in Social-Commerce Team evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Prove friends outperform unconnected; design metrics, observational analysis, and rollout experiment

  A Meta data-scientist technical-screen question on Analytics & Experimentation: using only info_stream_views and post_reactions, prove whether Friend-authored content is 'more social' than Unconnected content. It tests denominator-complete metric design with relationship attribution, observational causal validation (fixed effects + propensity/AIPW), and a network-aware rollout experiment with power, CUPED, interference mitigations, and guardrails — plus quantifying the long-term and discovery value of Unconnected content beyond near-term engagement.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Prove high-quality pixels improve ad performance

  This question tests your ability to design a rigorous causal experiment to validate a product hypothesis while accounting for selection bias, confounding variables, and operational constraints.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperiment DesignMetric Strategy
• MetaHard·Data Scientist
  Prove source growth is cannibalization, not incremental

  This question evaluates causal inference and experimental-design competencies in analytics, testing skills in identification strategy, treatment and control definition, specification of difference‑in‑differences or randomized geo experiments, metric construction, power calculations, and robustness checks for attribution and cannibalization analysis. It is commonly asked to determine whether observed growth is incremental or substitution-driven, falls under the Analytics & Experimentation domain in Data Science, and emphasizes practical application accompanied by conceptual understanding of identification assumptions, statistical power, and measurement.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Rank Ads by Conversion Rate for Top 10 Performers

  This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Redesign an executive dashboard for C-suite

  This question evaluates skills in dashboard design, data visualization, metric selection, and executive-level analytical communication for product and growth measurement.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Reduce variance with covariate adjustment

  This question evaluates a data scientist's competency in experimental design, variance reduction techniques, and regression-based covariate adjustment (CUPED/ANCOVA) within randomized A/B tests, including understanding of statistical power and diagnostic checks.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Reflect on Conflict Resolution and Key Learnings

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Reflect on Conflict Resolution and Key Learnings states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Replace legacy ads model safely

  This question evaluates competence in machine learning systems engineering, including offline validation, counterfactual/off‑policy evaluation, online experimentation design, and production monitoring for ad-ranking models.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Resolve Conflicts and Convince Skeptical Stakeholders Effectively

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Conflicts and Convince Skeptical Stakeholders Effectively states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• MetaMedium·Data Scientist
  Resolve Team Conflicts and Exceed Job Expectations Successfully

  Evaluates behavioral interview readiness for initiative beyond formal responsibilities and team conflict resolution. Strong answers use STAR, explain why the candidate stepped in, show stakeholder management, quantify impact, and resolve conflict without blame.

  Behavioral & LeadershipTechnical Screen
• MetaMedium·Data Scientist
  Resolve Ties for Top-10 Users in SQL Query

  This question evaluates proficiency in SQL ranking and tie-resolution techniques for generating deterministic top-N leaderboards, focusing on handling identical scores while producing an exact set of rows.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Resolve cross-team conflict and align incentives

  This question evaluates a Data Scientist's ability to resolve cross-team conflicts through stakeholder mapping, incentive alignment, quantified negotiation planning, meeting facilitation, escalation management, and success measurement.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Resolve exclusion, learn fast, and manage conflict

  This question evaluates interpersonal leadership, cross-team collaboration, conflict resolution, rapid domain learning, stakeholder mapping, and the ability to quantify analytic trade-offs and impact within a data science context.

  Behavioral & LeadershipOnsite
• MetaHard·Data Scientist
  Resolve teammate feeling unwelcome with measurable steps

  This question evaluates leadership competencies in managing psychological safety, conflict resolution, confidentiality, remote-team dynamics, evidence gathering, documentation, escalation, and outcome measurement.

  Behavioral & LeadershipOnsite
• MetaMedium·Data Scientist
  Retrieve Ad Metrics and Rates for Last 7 Days

  This question evaluates proficiency in SQL-based data manipulation including joins, conditional aggregation, time-based filtering, and computation of advertising metrics such as impressions, clicks, CTR, and conversion rate.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Retrieve Top Five Ads by Conversions in 30 Days

  This question evaluates a candidate's competency in SQL data manipulation, specifically aggregating event data and applying time-window filters to produce ranked metrics.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Run a clean A/B test for recommendations

  This question evaluates a data scientist's competency in experimental design, causal inference, and statistical power analysis for A/B tests, covering randomization unit choice, contamination prevention, metric and guardrail selection, clustering adjustments, and bias-control techniques.

  Analytics & ExperimentationOnsite
• MetaMedium·Data Scientist
  Select and prioritize metrics with guardrails

  This question evaluates a candidate's ability to design a robust metrics framework for product experiments, covering selection and normalization of success metrics, feature-only sizing and ecosystem metrics, explicit cannibalization reads, guardrails, multiple-testing control, and back-of-the-envelope LTV/ARPDAU impact estimation.

  Analytics & ExperimentationTechnical Screen
• MetaHard·Data Scientist
  Should We Launch Group Calling?

  A Meta Data Scientist technical-screen case: using only one-to-one call logs, decide whether to build a group calling feature and how to measure a launch's success. It tests product analytics, proxy-metric design, network analysis, causal inference, and experiment design under social-network interference.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Scientist
  Size opportunity for new product line

  This question evaluates a data scientist's skills in financial opportunity sizing, sensitivity analysis, and experimental design by requiring quantitative modeling of impacted users, incremental orders, revenue, and gross profit for a proposed product line.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Test if social users are more engaged

  This problem tests your ability to design an analytical framework to measure user engagement, account for selection bias in non-randomized groups, and define robust metrics for comparing heterogeneous user populations.

  Analytics & ExperimentationOnsiteAnalyticsCausal InferenceSelection Bias
• MetaMedium·Data Scientist
  Test two models' proportions for significance

  This question evaluates proficiency in statistical inference for proportions, covering hypothesis testing, confidence intervals, power and sample-size calculations, multiple-testing correction, and comparison of frequentist versus Bayesian approaches within the Statistics & Math domain for data scientist roles.

  Statistics & MathOnsite
• MetaMedium·Data Scientist
  Track Metrics to Measure Push Notification Quality

  Track Metrics to Measure Push Notification Quality evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• MetaHard·Data Scientist
  Track Success and Guardrail Metrics for Push Notifications

  Evaluates success and guardrail metrics for a push-notification feature in a networked travel app. Strong answers measure incremental itinerary engagement and retention, monitor opt-outs and fatigue, and design a cluster or graph-aware experiment to reduce spillovers.

  Analytics & ExperimentationTechnical Screen