Data Scientist Interview Questions

Showing 241–264 of 264 results

• UberMedium·Data Scientist
  Evaluate Promotions for Uber Eats Users

  This question tests the ability to design an A/B test for promotions in a two-sided marketplace, focusing on identifying interference (network effects) and selecting appropriate metrics to measure causal impact.

  Machine LearningTechnical ScreenCausal InferenceA/B TestingMarketplace Dynamics
• UberMedium·Data Scientist
  How would you build UberEats ranking?

  This question assesses the ability to design an end-to-end ranking system for a food delivery platform, focusing on objective formulation, feature selection, and the trade-offs between relevance and business constraints.

  Machine LearningTechnical ScreenRecommender SystemsLearning to RankFeature Engineering
• UberEasy·Data Scientist
  How would you design an ETA prediction system?

  This problem tests the ability to design an end-to-end machine learning system for predicting ride arrival times, focusing on feature selection, model architecture, and evaluation strategies.

  Machine LearningTechnical ScreenML System DesignRegressionFeature Engineering
• UberMedium·Data Scientist
  Implement Streaming Clustering for Numbers

  This question evaluates a data scientist's competency in online clustering for numeric streams, testing understanding of streaming algorithms, bounded-memory cluster summaries, online updates, and issues including initialization, convergence behavior, outlier handling, and concept drift.

  Machine LearningOnsite
• UpstartEasy·Data Scientist
  Derive logistic regression objective and gradients

  This question evaluates understanding of logistic regression, maximum likelihood estimation, cross-entropy (negative log-likelihood), L2 regularization, and the ability to compute model gradients for binary classification.

  Machine LearningHR Screen
• UpstartMedium·Data Scientist
  Design Push-Notification System for Airport Surge Pricing

  Design Push-Notification System for Airport Surge Pricing 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
• UpstartHard·Data Scientist
  Design a Real-Time Personalized Ad Selection System

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design a Real-Time Personalized Ad Selection System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• UpstartEasy·Data Scientist
  Explain L1 vs L2 and ridge vs lasso

  This question evaluates understanding of regularization techniques in Machine Learning—specifically distinctions between L1 and L2 norms and their instantiation as Lasso and Ridge regression—covering effects on objective functions, parameter sparsity, geometric intuitions, handling of correlated features, and the role of feature scaling.

  Machine LearningOnsite
• UpstartHard·Data Scientist
  How to Architect a Personalized Ads Serving System

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for How to Architect a Personalized Ads Serving System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• UpstartMedium·Data Scientist
  Leverage Existing Model for Low Credit Score Applicants

  Leverage Existing Model for Low Credit Score Applicants 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 LearningOnsite
• VoleonHard·Data Scientist
  Build a regularized regression pipeline

  This question evaluates a candidate's competency in building an end-to-end regularized regression pipeline in scikit-learn, covering feature engineering and polynomial expansion, categorical encoding, target‑transform diagnostics, penalized linear model selection and tuning (Ridge/Lasso/ElasticNet), cross‑validation strategies including TimeSeriesSplit, leakage prevention, nested evaluation, feature effect ranking, and model persistence. Commonly asked in Machine Learning interviews for Data Scientist roles, it tests practical application skills in the Machine Learning domain and requires both hands-on implementation proficiency and conceptual understanding of validation design, target transformations, and interpretability, with emphasis on practical application augmented by conceptual judgment.

  Machine LearningTechnical Screen
• VoleonMedium·Data Scientist
  Describe Your Machine Learning Project Experience

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Describe Your Machine Learning Project Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningHR Screen
• VoleonHard·Data Scientist
  Design and diagnose a regression pipeline

  This question evaluates a data scientist's ability to design and diagnose an end-to-end regression pipeline for zero-inflated, heavy-tailed targets, focusing on feature engineering for high-cardinality variables, handling multicollinearity, choosing appropriate loss/distribution and regularization, time-aware validation, uncertainty quantification, interpretability, and data leakage mitigation. It is commonly asked in the Machine Learning domain to test practical application alongside conceptual understanding of regression modeling, distributional assumptions, cross-validation strategies, and performance versus interpretability trade-offs.

  Machine LearningTechnical Screen
• VoleonMedium·Data Scientist
  Fit Linear Regression: Analyze Economic Impact of Coefficients

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Fit Linear Regression: Analyze Economic Impact of Coefficients states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• Walmart LabsMedium·Data Scientist
  Predict Next-Period Conversion Rate Using Historical Campaign Data

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Predict Next-Period Conversion Rate Using Historical Campaign Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• 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
  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
• 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
  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
  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
• YYelpHard·Data Scientist
  Design a Cold-Start-Aware Recommender

  This question evaluates a data scientist's expertise in production recommender systems, covering cold-start strategies, two-stage retrieval and ranking model design, multimodal feature engineering, exploration–exploitation, bias-corrected experimentation, and low-latency serving.

  Machine LearningOnsite
• ZAZest AIHard·Data Scientist
  Build a fair loan classifier

  This question evaluates competency in supervised classification and credit risk modeling, covering data exploration and preparation, model selection, evaluation metrics, threshold setting, fairness and regulatory considerations, interpretability, validation, and monitoring.

  Machine LearningOnsite
• ZAZest AIHard·Data Scientist
  Diagnose overfitting from error curves

  This question evaluates the ability to recognize and diagnose model generalization problems—specifically overfitting—by interpreting training, validation, and test error curves and understanding validation procedures and sources of variance.

  Machine LearningOnsite
• ZooxMedium·Data Scientist
  Design navigation-safety simulation parameters and experiments

  This problem tests your ability to design a high-reliability simulation framework using variance reduction techniques and statistical validation methods to evaluate rare-event autonomous driving scenarios.

  Machine LearningTechnical ScreenSimulationRare-Event-EstimationImportance-Sampling