Data Scientist Interview Questions

Showing 151–180 of 264 results

• MetaHard·Data Scientist
  Detect leakage and evaluate a prediction model

  This question evaluates competence in production-ready supervised machine learning—detecting and repairing data leakage in logs-based features, designing time-aware cross-validation, defining KPI-driven thresholding and calibration, interpreting model coefficients, and establishing monitoring and retraining policies for a churn prediction model in the Machine Learning domain. It is commonly asked because interviewers need to assess both conceptual understanding of leakage, validation, and calibration principles and practical application skills for deploying and maintaining models with cost-sensitive objectives and monitoring in production, so the level of abstraction spans conceptual reasoning and hands-on practical application.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Employ Collaborative Filtering for Personalized Recommendation Lists

  Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Evaluate Classifier with Precision, Recall, and Fairness Metrics

  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 Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Data Scientist
  Evaluate Factors Before Replacing Recommendation Model

  Evaluates whether an ads recommendation model should replace an existing production model. Strong answers combine offline validation, online experiments, revenue and advertiser outcomes, user guardrails, conflicting metric interpretation, rollout safety, rollback planning, and executive communication.

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Evaluate Fake-Account Classifier with Precision and Recall Metrics

  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 Fake-Account Classifier with Precision and Recall Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• MetaHard·Data Scientist
  How to design Shop ad ranking

  This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.

  Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems
• MetaEasy·Data Scientist
  How would you design a Shop Ads ranking algorithm?

  This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.

  Machine LearningTechnical ScreenSystem DesignRankingAds
• MetaMedium·Data Scientist
  How would you predict a car’s turning intention?

  This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).

  Machine LearningOnsite
• MetaMedium·Data Scientist
  Identify Algorithms for Detecting Malicious Duplicated Content

  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 Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Data Scientist
  Identify Features for Fake News Detection on Facebook

  Identify Features for Fake News Detection on Facebook 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
• 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
  What features and feature selection would you use?

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Which clustering algorithm would you use and why

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Build and Defend a Baseline Model from a CSV

  Tests end-to-end applied machine learning design on tabular data: framing the prediction target, eliminating data leakage, establishing a leakage-free validation strategy, selecting baselines and evaluation metrics, and planning deployment monitoring and rollback.

  Machine LearningOnsiteModel EvaluationData LeakageMLOps
• MicrosoftHard·Data Scientist
  Compare CNN/RNN/LSTM and implement K-means

  This question evaluates understanding of deep learning architectures (CNN, RNN, LSTM) and unsupervised clustering implementation (K-means), covering inductive biases, parameter sharing and receptive fields, LSTM gating and vanishing-gradient behavior, tensor shape reasoning, algorithmic complexity, initialization, and cluster handling.

  Machine LearningOnsite
• MicrosoftEasy·Data Scientist
  Compute and plot a precision–recall curve

  This question evaluates understanding of binary classification evaluation metrics—precision, recall, precision–recall curves, and related summaries like Average Precision/AUPRC—within the Machine Learning domain and is relevant for Data Scientist roles.

  Machine LearningTechnical Screen
• MicrosoftHard·Data Scientist
  Design a model for imbalanced conversions

  This question evaluates a data scientist's ability to design and validate end-to-end predictive models for imbalanced binary outcomes, encompassing feature engineering, class imbalance handling, probability calibration, thresholding for budgeted targeting, validation, monitoring, and interpretability.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Explain Deep Learning to a 5-Year-Old Child

  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 Explain Deep Learning to a 5-Year-Old Child states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and PCA and key tradeoffs

  Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and how to tune it

  This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain SHAP and build an ML project

  This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.

  Machine LearningTechnical ScreenSHAPInterpretabilityMLOps
• MicrosoftMedium·Data Scientist
  Explain SHAP in an ML System

  This question evaluates a data scientist's competence in designing and operationalizing end-to-end machine learning systems in the Machine Learning domain, covering problem framing, label and feature design, model selection, evaluation, deployment, monitoring, and model interpretability using SHAP.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  How would you build and evaluate a classifier?

  This problem tests your ability to design a binary classification pipeline from metric selection to production monitoring, with a focus on aligning technical decisions with business objectives.

  Machine LearningTechnical ScreenClassificationModel EvaluationImbalanced Data
• MixpanelMedium·Data Scientist
  Validate Features That Predict User Retention

  Learn to evaluate retention features with leakage-safe time windows, cohort validation, baselines, ablations, and calibrated predictions.

  Machine LearningTechnical Screen
• Morgan StanleyMedium·Data Scientist
  Answer basic probability and statistics questions

  This problem tests your conceptual understanding of probability distributions, statistical bias in data collection, and the relationship between covariance and linear correlation.

  Machine LearningOnline AssessmentStatisticsProbabilityBias
• NVIDIAHard·Data Scientist
  Diagnose overfitting, DenseNet, preprocessing, CV

  This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.

  Machine LearningHR Screen
• NetflixMedium·Data Scientist
  Address Fraud Detection with Imbalance and Concept Drift Solutions

  Evaluates end-to-end fraud detection ML system design for online payments under drift and imbalance. Strong answers handle delayed labels, class imbalance, feature engineering, cost-sensitive validation, real-time deployment, threshold actions, monitoring, retraining, and concept drift.

  Machine LearningOnsite
• NetflixHard·Data Scientist
  Design a robust conversion propensity model

  This question evaluates a candidate's competency in end-to-end propensity modeling, covering correct labeling to avoid post-treatment leakage, time-windowed feature engineering, class-imbalance and calibration strategies, causal uplift estimation, and production concerns such as monitoring, serving consistency, and cold-start scoring.

  Machine LearningOnsite
• OneMain FinancialEasy·Data Scientist
  Explain decision trees and tree ensembles

  This question evaluates understanding of decision trees, splitting criteria for classification versus regression, hyperparameter effects on the bias–variance trade-off, and the mechanics and rationale behind tree-based ensemble methods in the Machine Learning domain.

  Machine LearningTechnical Screen
• OneMain FinancialHard·Data Scientist
  Select and tune XGBoost hyperparameters

  This question evaluates skills in selecting and tuning XGBoost hyperparameters, managing severe class imbalance and sparse one‑hot encodings, handling missing values, and designing compute‑efficient training and grouped cross‑validation to prevent user‑level leakage.

  Machine LearningTechnical Screen