Data Scientist Interview Questions

Showing 1921–1950 of 1971 results

• WaymoMedium·Data Scientist
  Evaluate Smart Wait Launch Impact

  This question evaluates a data scientist's competencies in causal inference and experiment analysis, including interpreting A/B test outcomes, trade-offs between conversion and operational metrics, selection of primary and guardrail metrics, and generation of alternative hypotheses.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Evaluate a New Version in Simulation and Explain a Flat Live Result

  Compare model versions on paired simulation scenarios and diagnose why offline improvements may disappear in live rare-event evaluation.

  Analytics & ExperimentationTechnical Screen
• WaymoEasy·Data Scientist
  How predict vehicles’ turn direction at intersection?

  This problem tests the ability to design an end-to-end machine learning pipeline for vehicle intent prediction, focusing on feature engineering, sequence modeling, and temporal validation.

  Machine LearningOnsiteTime-SeriesFeature EngineeringAutonomous Driving
• WaymoEasy·Data Scientist
  How validate a driving simulation is realistic?

  This question tests the ability to design an evaluation framework to compare simulated vehicle behavior against real-world sensor and trajectory logs to ensure high-fidelity simulation.

  Analytics & ExperimentationOnsiteSimulationValidationStatistics
• WaymoEasy·Data Scientist
  Implement K-means and handle train-inference mismatch

  This question evaluates understanding and hands-on competency in unsupervised clustering (K-means objective, alternating optimization, initialization strategies, empty-cluster handling, stopping criteria, and computational complexity) and sequence modeling for multi-agent trajectory prediction (input representation, attention/GNN architectures, deterministic vs probabilistic outputs, evaluation metrics, and exposure bias from autoregressive training). It is commonly asked to probe both conceptual understanding and practical implementation skills in the Machine Learning domain, testing optimization and scalability trade-offs as well as model-design and training/inference mismatch considerations at a mix of conceptual and practical application abstraction levels.

  Machine LearningTechnical Screen
• WaymoMedium·Data Scientist
  Implement Safe Average Function

  This question evaluates proficiency with basic programming fundamentals including numeric computation, handling edge cases such as empty inputs, input immutability, and data type consistency.

  Coding & AlgorithmsOnline Assessment
• WaymoMedium·Data Scientist
  Implement a Safe Average Function in Python

  Implement a Python average function that returns zero for an empty list and explain its time and space complexity. Clarify numeric types, missing values, NaN behavior, streaming alternatives, precision concerns, and the tests needed for a reliable data utility.

  Data Manipulation (SQL/Python)Online Assessment
• WaymoEasy·Data Scientist
  Implement nested template string substitution

  This problem tests your ability to handle recursive data structures and string parsing by expanding nested placeholders within a template string while identifying potential circular dependencies.

  Coding & AlgorithmsOnsiteRecursionString ProcessingMemoization
• WaymoMedium·Data Scientist
  Interpret a Smart Wait Launch with Conflicting Metrics

  Interpret a product launch where conservative wait estimates reduce conversion while observed pickup time also falls. Diagnose selection effects and alternative causes, define customer and fleet metrics with consistent denominators, and propose an experiment that can separate display accuracy from operational improvement.

  Analytics & ExperimentationOnline Assessment
• WaymoEasy·Data Scientist
  Merge overlapping intervals per group in pandas

  This problem tests your ability to handle temporal data by merging overlapping time intervals within distinct groups using pandas and custom logic.

  Data Manipulation (SQL/Python)OnsitePandasData ManipulationIntervals
• WaymoMedium·Data Scientist
  Model Rare Events with a Log Link and Handle Zero Counts

  Explain rare-event log-link models, exposure offsets, zero counts, overdispersion, and the difference between counts and binary outcomes.

  Statistics & MathTechnical Screen
• WaymoEasy·Data Scientist
  Model wins-until-failure and expected future wins

  This question tests your ability to model geometric distributions and apply maximum likelihood estimation (MLE) to censored data.

  Statistics & MathOnsiteStatisticsMLEProbability
• WaymoMedium·Data Scientist
  SQL: Average Ride Rating in Users' First and Third Active Months by City

  Write a PostgreSQL query that compares average ride ratings in each user's first and third active months, grouped by city and averaged over individual rides. Tests ranking distinct active months per user, window functions, NULL ratings and weighting.

  Data Manipulation (SQL/Python)Onsite
• WaymoMedium·Data Scientist
  SQL: Users With No Rides in the Most Recent 7-Day Window, Including Non-Riders

  Write a PostgreSQL query that lists users with no rides in the most recent 7-day window, including users who never rode. Tests anchoring a date window to the data, inclusive boundaries, and anti-join logic that keeps users without matching rides.

  Data Manipulation (SQL/Python)Onsite
• WaymoMedium·Data Scientist
  Sample Points Uniformly from a Unit Disk

  Derive uniform random sampling inside a unit disk, explain the radial distribution, and compare a polar sampler with rejection sampling.

  Coding & AlgorithmsTechnical Screen
• WaymoMedium·Data Scientist
  Test Whether a Routing Experiment Reduced Pickup Time

  Analyze a routing A/B test in Python by filtering and joining user and ride data, then calculating a Welch t-test for pickup time. Interpret effect size and uncertainty, address repeated rides and post-treatment selection, and design stronger user-level or cluster-aware inference.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Welch's t-Test p-Value for a Routing Experiment on Time to Pickup, and Its Validity

  Filter rides to one city and month, join users to their experiment variants, and compute a Welch's t-test p-value on time to pickup in Python. Then judge whether the routing test supports a conclusion and design a better experiment for a shared fleet.

  Analytics & ExperimentationOnsite
• WeRideMedium·Data Scientist
  Compute window averages and merge intervals

  Compute window averages and merge intervals 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.

  Data Manipulation (SQL/Python)Technical Screen
• WeRideMedium·Data Scientist
  Test whether two distributions differ

  Test whether two datasets come from the same distribution using univariate and multivariate methods, including KS, categorical tests, classifier two-sample tests, MMD, permutation tests, effect sizes, multiple-testing control, clustering-aware uncertainty, and sampling designs.

  Statistics & MathTechnical Screen
• WAWeiss Asset ManagementMedium·Data Scientist
  Evaluate and Monitor a Paid Electricity Price Forecast

  This problem tests the ability to design an evaluation framework for time-series forecasting models and systematic troubleshooting of performance degradation in an automated trading pipeline.

  Analytics & ExperimentationTechnical ScreenTime SeriesBacktestingModel Evaluation
• Wells FargoHard·Data Scientist
  Core Components of an LLM-Based AI Agent and How They Interact

  An open question about AI agents asks you to name the components of an agent built around a language model, explain what each one does, and show how they work together to complete a multi-step task. It tests understanding of tools, memory, planning, control loops, guardrails and evaluation.

  Machine LearningTechnical Screen
• Wells FargoMedium·Data Scientist
  Design Excel visuals for risk results

  Design Excel visuals for risk results 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 & ExperimentationTechnical Screen
• Wells FargoMedium·Data Scientist
  Ensure model ethics and governance

  Ensure model ethics and governance 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
• Wells FargoMedium·Data Scientist
  Explain PD model validation steps

  Explain PD model validation steps evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• Wells FargoHard·Data Scientist
  Organizing Code and Repositories: Functions, Classes, Comments, GitHub Files

  A software practice question asks how you organize code into functions, classes and files, how and when you write comments, and which files a GitHub repository should and should not contain. It tests modular design judgment, documentation habits, and reproducible, secure project hygiene.

  Software Engineering FundamentalsTechnical Screen
• Wells FargoHard·Data Scientist
  Research Deep Dive: Model Details, Coding Habits and Diffusion Architecture

  A research deep dive asks you to present a project under repeated follow-ups about the exact model you used and your coding habits, then explain how a diffusion model is built, trained and sampled. It tests ownership of your work, depth under probing, and command of noising, denoising networks and conditioning.

  Machine LearningTechnical Screen
• Wells FargoMedium·Data Scientist
  Write SQL for rolling default rates

  This question evaluates proficiency with SQL window functions and time-series aggregation, specifically the ability to compute rolling metrics across customer segments by aggregating defaults and accounts over a moving 12-month window.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Calculate Monthly Customer Retention

  A PostgreSQL interview problem about calculating monthly customer retention from activity data. It tests month-level cohort construction, distinct-user counting, correct denominator selection, and handling customers who return across calendar boundaries.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Compute a Seven-Day Rolling Average

  A PostgreSQL interview problem about computing a seven-day rolling average from dated observations. Candidates must use window frames carefully, preserve the requested grain, handle incomplete history, and return deterministically ordered results.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Define Marketplace Success Metrics and Investigate Their Movement

  A product analytics interview about defining success for a two-sided marketplace and diagnosing an unexpected metric movement. It tests metric decomposition, cohort and funnel analysis, instrumentation checks, and the ability to separate product effects from mix shifts.

  Analytics & ExperimentationTechnical Screen