Data Scientist Interview Questions

Showing 691–720 of 1971 results

• FMFannie MaeMedium·Data Scientist
  Assess SQL joins, unions, windows, dedup, and pandas

  This question evaluates proficiency in SQL and pandas data-manipulation techniques, including aggregations with NULL semantics, various join types, set operations and deduplication, window functions, and reconciling normalized and denormalized sources.

  Data Manipulation (SQL/Python)Technical Screen
• FMFannie MaeMedium·Data Scientist
  Design Test Cases for UI Change: Ensuring Quality and Functionality

  Evaluates mission-aware QA collaboration for UI and backend changes in a data-science role. Strong answers compare test layers, define acceptance criteria, cover accessibility and auditability, and design robust test cases.

  Behavioral & LeadershipOnsite
• FMFannie MaeMedium·Data Scientist
  Understand SQL Aggregations and Joins: Key Differences Explained

  This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.

  Data Manipulation (SQL/Python)Onsite
• Fetch RewardsMedium·Data Scientist
  Design analysis to validate reported decline

  This question evaluates analytical and experimental-design competencies including metric specification, time-series and same-store comparisons, bias and confounder detection, and concise data-driven communication under a tight time constraint.

  Analytics & ExperimentationTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Evaluate Overlapping Shelf Ranking Experiments

  This question evaluates competency in experimental design and causal inference for A/B testing, focusing on handling overlapping traffic, interaction effects between concurrent experiments, metric selection, guardrails, and statistical validity.

  Analytics & ExperimentationTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Handle unresponsive stakeholders and align metrics

  This question evaluates stakeholder management, metric design and alignment, ambiguity handling, and decision-making communication skills for a data scientist.

  Behavioral & LeadershipTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Write SQL to verify quarterly sales decline

  This question evaluates proficiency in SQL aggregation, time-series period-over-period comparison, same-store cohort analysis, correct grain handling, and treatment of returns, testing data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• FigmaMedium·Data Scientist
  Clarify an Ambiguous Product Question and Define a Metric

  This question tests the ability to transform an abstract product objective into a rigorous measurement framework, focusing on metric selection and experimental design.

  Analytics & ExperimentationOnsiteAnalyticsProduct MetricsExperimentation
• FigmaMedium·Data Scientist
  Deliver a crisp self-introduction

  This question evaluates a candidate's ability to deliver a concise, metric-backed self-introduction demonstrating product-analytics impact, cross-functional influence, leadership presence, and alignment with a collaborative design SaaS mission.

  Behavioral & LeadershipHR Screen
• FigmaMedium·Data Scientist
  Describe impact, prioritization, and stakeholder management

  Describe impact, prioritization, and stakeholder management 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 & LeadershipTechnical Screen
• FigmaHard·Data Scientist
  Describe your most and least engaging work

  This question evaluates self-awareness, communication, motivation, collaboration, and role-alignment competencies that reveal which problem types and team environments enable a data scientist to perform best.

  Behavioral & LeadershipTechnical Screen
• FigmaMedium·Data Scientist
  Handle Cross-Functional Pushback on an Analysis

  This question assesses your ability to navigate professional disagreement, exercise empathy towards cross-functional stakeholders, and leverage data to resolve conflicting viewpoints.

  Behavioral & LeadershipOnsiteBehavioralCommunicationConflict Resolution
• FigmaHard·Data Scientist
  How would you measure product success?

  This question tests the ability to define a comprehensive measurement framework for a B2B SaaS collaborative tool, balancing growth, engagement, and retention metrics.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsMetrics DesignSaaS
• FigmaMedium·Data Scientist
  Negotiate Figma compensation analytically

  This question evaluates quantitative compensation analysis, negotiation strategy, and stakeholder communication skills for a Data Scientist role, focusing on modeling offer components and articulating numerical trade-offs.

  Behavioral & LeadershipHR Screen
• FigmaMedium·Data Scientist
  Present a Relevant Data Science Project to a Hiring Manager

  This question assesses a candidate's ability to communicate technical complexity to a business audience, demonstrate ownership, and articulate the link between statistical results and product outcomes.

  Behavioral & LeadershipOnsiteBehavioralProject-WalkthroughCommunication
• FAFirst AmericanMedium·Data Scientist
  Clarify Ambiguous Requirements and Resolve Team Conflicts

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

  Behavioral & LeadershipOnsite
• FRFive RingsHard·Data Scientist
  Solve coin probability and Bayesian inference problems

  This question evaluates probabilistic reasoning, stochastic-process intuition (pattern waiting times and runs), expected-value calculations, and Bayesian parameter estimation competency.

  Statistics & MathOnline Assessment
• FHFlatiron HealthMedium·Data Scientist
  Build a robust ML pipeline

  This question evaluates a candidate's competency in designing robust end-to-end ML pipelines, covering temporal data slicing, leakage controls, time-series cross-validation, feature store consistency, offline and business metrics, drift detection and monitoring, online rollout strategies, retraining triggers, and fairness assessment.

  Machine LearningTechnical Screen
• FHFlatiron HealthMedium·Data Scientist
  Compute churn metrics and rank top students

  This question evaluates proficiency in data cleaning, transformation, aggregation, and deterministic ranking across R (tidyverse) and SQL, focusing on handling missing values, date arithmetic, aggregation and rounding of metrics, and top-N selection with tie-breaking.

  Data Manipulation (SQL/Python)Technical Screen
• FHFlatiron HealthMedium·Data Scientist
  Write SQL window functions for streaks

  This question evaluates proficiency with SQL window functions, date arithmetic, gap-and-island (streak) detection, rolling aggregates, and event-order sequencing to derive DAU, revenue metrics, and inactivity gaps.

  Data Manipulation (SQL/Python)Technical Screen
• Freddie MacMedium·Data Scientist
  Compute leakage-safe rolling features in pandas

  This question evaluates proficiency in time-series feature engineering, leakage prevention, memory-efficient large-scale data processing, categorical target encoding, exponential weighting, and algorithmic complexity within the Data Manipulation (SQL/Python) domain, emphasizing practical application with pandas at scale.

  Data Manipulation (SQL/Python)Onsite
• Freddie MacMedium·Data Scientist
  Implement blocked time-series cross-validation

  This question evaluates competence in designing panel-aware time-series cross-validation with embargo handling, grouped blocking, deterministic splitting, scalability analysis, and adversarial unit-test design for issues like duplicate timestamps and entity migration.

  Coding & AlgorithmsOnsite
• GGeminiMedium·Data Scientist
  Detect k events in a sliding window

  This question evaluates a candidate's ability to design efficient sliding-window and streaming algorithms for time-series event data, encompassing deduplication, handling bounded out-of-order arrivals and clock skew, distinct-device counting, and meeting specified time and space complexity constraints.

  Coding & AlgorithmsOnsite
• GleanEasy·Data Scientist
  Compute DAU and rolling MAU with zero days

  This question evaluates a data scientist's proficiency in time-series analytics and data aggregation, focusing on computing daily active users and a 30-day rolling MAU from user and login tables, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• GleanMedium·Data Scientist
  Expectation and Variance of a Sum of Two Random Variables, Including Covariance

  Asks how to compute the expectation and variance of the sum of two random variables, in general and when they are independent. Tests derivation from definitions, the role of covariance, generalizing to weighted sums and differences, and applying the result to data-science problems such as comparing group means.

  Statistics & MathOnsite
• GleanEasy·Data Scientist
  How to measure product success?

  This question evaluates a data scientist's competency in product analytics, metric definition, experiment design, and causal inference, including articulation of a north-star metric, supporting funnel and guardrail metrics, and handling confounding, seasonality, and heterogeneous effects.

  Analytics & ExperimentationOnsite
• GleanMedium·Data Scientist
  Product Case: Define Success Metrics for a Product, Using Enterprise Search as Example

  A data scientist product case: define how to measure the success of a product, practiced on an enterprise search tool. Tests building a metric hierarchy with a north star, input metrics and guardrails, separating users from paying customers, defining a successful search and attributing changes causally.

  Analytics & ExperimentationOnsite
• GleanMedium·Data Scientist
  SQL: Zero-Filled DAU, Rolling 30-Day MAU, and the MAU Impact of a User-ID Rehash

  Three SQL questions on a login table: daily active users with zero-filled days, rolling 30-day MAU per date with a weekday-usage follow-up, and how a user-ID rehash inflates MAU. Tests distinct counting, calendar generation, window boundaries, query cost and reasoning about metric artifacts.

  Data Manipulation (SQL/Python)Onsite
• GleanEasy·Data Scientist
  What are sum expectation and variance?

  This question evaluates understanding of expectation, variance and covariance and the ability to reason about how sums of random variables affect their moments, including the role of covariance and the special case of independence in the Statistics & Math domain.

  Statistics & MathOnsite
• GGofundmeMedium·Data Scientist
  Explain an AI Agent You Built: Design, Evaluation, and Governance

  Practice explaining an AI-agent project through design ownership, task evaluation, hallucination controls and data governance.

  ML System DesignOnsite