Data Scientist Interview Questions

Showing 1–30 of 76 results

• AmazonHard·Data Scientist
  Build a package-allocation model for couriers

  This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.

  Machine LearningOnsite
• AmazonHard·Data Scientist
  Design a Machine Learning Recommendation System Pipeline

  Design a Machine Learning Recommendation System Pipeline 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
• AmazonHard·Data Scientist
  Design an ML Model for Interview Recommendation Pipeline

  Design an ML Model for Interview Recommendation Pipeline 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
• AmazonHard·Data Scientist
  Design end-to-end regression for energy demand

  This question evaluates a candidate's ability to design and justify an end-to-end regression system for next-day site-level energy demand forecasting, testing competencies in time-series modeling, feature engineering, regularization, evaluation metrics and SLAs, production ML pipelines, monitoring, explainability, and data privacy.

  Machine LearningOnsite
• AmazonHard·Data Scientist
  Design fraud detection across channels with unknowns

  This question tests the ability to design an end-to-end fraud detection system that accounts for cold-start problems, cross-channel signals, and the trade-off between customer friction and loss mitigation.

  Machine LearningOnsiteFraud DetectionAnomaly DetectionGraph Neural Networks
• AmazonMedium·Data Scientist
  Diagnose Bias–Variance Trade-off in Supervised Learning

  Diagnose Bias–Variance Trade-off in Supervised Learning 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
• AmazonEasy·Data Scientist
  Evaluate NLP Classification Models

  This question evaluates competency in NLP classification model evaluation, covering understanding of confusion matrices, precision/recall/F1/AUC metrics, thresholding and trade-offs, handling class imbalance, and connecting metric choices to operational actions and business costs.

  Machine LearningOnsite
• AmazonMedium·Data Scientist
  Optimize Email Strategy for New Prime Video Series Launch

  Evaluates email targeting and ranking strategy for a new Prime Video series launch. Strong answers cover data needs, cold start, ranking models, offline metrics, A/B testing, guardrails, and model selection.

  Machine LearningOnsite
• AmazonMedium·Data Scientist
  Optimize XGBoost for Predicting Marketing Outcomes

  Optimize XGBoost for Predicting Marketing Outcomes 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
• AmazonMedium·Data Scientist
  Write and explain gradient descent pseudocode

  This question evaluates a candidate's understanding of batch gradient descent for linear regression with an intercept, covering concepts such as mean squared error optimization, vectorized matrix operations, analytical gradient computation, learning rate schedules, stopping criteria, feature scaling, and L2 regularization.

  Machine LearningOnsite
• AppleMedium·Data Scientist
  Explain CNN shapes, params, and trade-offs

  This question tests your ability to calculate output dimensions, parameter counts, and computational costs (FLOPs/MACs) in CNNs, while requiring you to justify architectural choices like depthwise convolutions and normalization layers.

  Machine LearningOnsiteCNNDeep LearningModel Architecture
• AppleHard·Data Scientist
  Implement random forest with OOB and imbalance

  This question evaluates expertise in implementing and engineering a memory- and compute-efficient Random Forest for binary classification, covering ensemble methods, CART/Gini impurity, OOB evaluation and calibration, class-imbalance handling, reproducibility, parallel and thread-safe system design, and streaming/warm-start model updates.

  Machine LearningOnsite
• BByteDanceHard·Data Scientist
  How to deploy and tune multimodal models?

  A six-part ByteDance data scientist onsite ML interview covering deploying and tuning multimodal models under GPU/VRAM, compute, and latency constraints; designing fast video retrieval over precomputed captions and embeddings with ANN and hybrid reranking; and deep-learning fundamentals — overfitting detection and mitigation, dropout and inverted-dropout scaling, normalization layers (BatchNorm/LayerNorm/GroupNorm/InstanceNorm/RMSNorm), and reinforcement learning for LLM post-training (RLHF). It tests practical systems tradeoffs alongside theoretical foundations across ML systems, retrieval, and reinforcement learning.

  Machine LearningOnsite
• C3 AIMedium·Data Scientist
  Explain key ML/stats concepts

  Explain key ML/stats concepts 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
• Capital OneEasy·Data Scientist
  Build House Price Model Responsibly

  This problem tests your ability to design a robust, fair, and scalable machine learning pipeline for house price prediction, emphasizing data preprocessing, model selection, and ethical oversight.

  Machine LearningOnsiteSupervised LearningRegressionFairness
• Capital OneMedium·Data Scientist
  Build and evaluate donation propensity model

  This question evaluates a data scientist's competency in profit-oriented predictive modeling, including conversion and donation-amount modeling, handling sparse and categorical features, calibration and uplift estimation, and offline policy evaluation under selection bias.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Design ML deployment with GitHub and Jenkins

  This question tests the ability to design an end-to-end MLOps pipeline, focusing on CI/CD orchestration, model versioning, reproducibility, and security compliance in a regulated financial environment.

  Machine LearningOnsiteMLOpsCI/CDSystem Design
• Capital OneHard·Data Scientist
  Design a production face recognition system

  This question evaluates production ML system design and engineering skills specific to face recognition, covering on-device model selection and embedding design, training objectives and evaluation protocols, anti-spoofing and robustness mechanisms, privacy and template protection, performance/resource constraints, fairness and monitoring, and safe experimentation. It is asked in the Machine Learning domain for Data Scientist roles because it assesses trade-offs between privacy, security, latency, and scalability in real-world systems, and it tests both conceptual understanding of biometric and security principles and practical application for deploying and operating on-device ML.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Diagnose Multicollinearity in Flight Delay Prediction Model

  Evaluates flight-delay modeling, data quality checks, and multicollinearity diagnosis. Strong answers identify leakage and timestamp issues, choose classification or regression based on the target, diagnose VIF and correlated features, and mitigate multicollinearity based on prediction or interpretation goals.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Evaluate Models for Credit-Risk Scoring at Capital One

  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 Evaluate Models for Credit-Risk Scoring at Capital One states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Evaluate OutlierHandler Class for Code Quality and Testing

  Evaluates code-review judgment for OutlierHandler and imputer preprocessing classes in ML pipelines. Strong answers explain fit-transform separation, leakage prevention, API quality, edge cases, and testing.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Evaluate Python Class Design in Data Pipeline

  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 Evaluate Python Class Design in Data Pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• Capital OneMedium·Data Scientist
  Identify Risks and Improve Imputation Class Implementations

  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 Identify Risks and Improve Imputation Class Implementations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Build ETA prediction and simulate impact

  This question evaluates machine learning competencies for ETA prediction, including feature engineering, leakage detection, time-based validation, point and quantile modeling, evaluation and calibration metrics, cost-sensitive decisioning, explainability and fairness auditing, and production deployment considerations.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Build a late-delivery risk model

  This question evaluates competencies in production-ready predictive modeling, including target definition, feature engineering, temporal train/validation splitting to avoid leakage, model evaluation and calibration, monitoring and ramp strategies, and translating probabilistic outputs into business-impacting decisions for a Data Scientist.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Design a Homepage Store Recommender

  This question assesses the ability to design an end-to-end, low-latency recommendation system for a food delivery marketplace, focusing on candidate retrieval strategies, real-time personalization, and system architecture.

  Machine LearningOnsiteRecommender SystemsSystem DesignRanking
• DoorDashHard·Data Scientist
  Design a Low-Latency Store Recommender

  This question assesses the ability to architect a multi-stage recommendation pipeline that balances low-latency retrieval with high-accuracy ranking in a geo-aware, high-concurrency food delivery context.

  Machine LearningOnsiteSystem DesignRecommendation SystemsLatency
• DoorDashHard·Data Scientist
  Design and evaluate an uplift model

  This question evaluates competency in causal inference and uplift modeling, experimental design for randomized treatment allocation, offline evaluation of uplift models, operational decision rules under budget constraints, fairness guardrails, and handling interference and deployment of sequential tests in a data-science context.

  Machine LearningOnsite
• ExpediaHard·Data Scientist
  Validate and monitor ranking model end-to-end

  This question evaluates a data scientist's mastery of learning-to-rank concepts, offline evaluation and metric design, position-bias correction, diagnostic analysis, deployment safeguards, and alignment of surrogate objectives with business KPIs for hotel search ranking.

  Machine LearningOnsite
• GoogleMedium·Data Scientist
  Address Overfitting in Supervised Learning Models

  Evaluates supervised learning judgment around bias, variance, overfitting, and train-test performance gaps. Strong answers diagnose leakage, split errors, distribution shift, and high variance, then propose regularization, simpler models, validation, more data, and monitoring.

  Machine LearningOnsite