Data Scientist Interview Questions

Showing 1–13 of 13 results

• C3 AIHard·Data Scientist
  Evaluate a Retrieval-Augmented Generation System

  Design an end-to-end evaluation framework for an enterprise RAG system that assesses retrieval quality, generative fidelity, claim-level attribution, security controls, and production telemetry.

  ML System DesignTechnical ScreenRAGLLM EvaluationSystem Design
• C3 AIHard·Data Scientist
  Forecast Food Stocking Needs Under Waste and Stockout Costs

  Design an end-to-end inventory forecasting and stocking optimization system for perishable food items under asymmetric costs of spoilage versus stockouts. The design covers handling censored sales data, probabilistic demand modeling, cost-optimal stocking policies, and validation strategy.

  ML System DesignTechnical Screenml-system-designtime-seriessupply-chain
• C3 AIHard·Data Scientist
  Reduce Fresh-Food Waste with an End-to-End ML Approach

  This problem tests the ability to design an integrated ML system that moves beyond simple demand forecasting by addressing inventory dynamics, supply constraints, and decision optimization.

  ML System DesignOnsiteSupply ChainOptimizationCensored Data
• Capital OneMedium·Data Scientist
  Explain an ML Project from Model Development Through Deployment

  This question assesses your ability to articulate the end-to-end lifecycle of an ML project, focusing on the bridge between technical modeling choices and real-world business impact.

  ML System DesignTechnical ScreenML LifecycleModel DeploymentSystem Design
• CitadelHard·Data Scientist
  Build a regression model for wind power output

  This question evaluates competency in feature engineering, physics-informed regression modeling, sensor-data quality handling, model selection and validation, and uncertainty estimation for turbine-level instantaneous power prediction.

  ML System DesignOnline Assessment
• CitadelHard·Data Scientist
  Design a time-series home-buy decision classifier

  This question evaluates competency in ML system design for time-series decisioning, including rigorous target and horizon definition, temporal feature engineering, time-aware validation, probabilistic calibration and thresholding, leakage controls, model selection trade-offs, and deployment monitoring.

  ML System DesignOnline Assessment
• CitadelMedium·Data Scientist
  Stabilize LLM inference and estimate needed repeats

  This question tests the ability to design an LLM inference pipeline that ensures result stability through statistical aggregation and estimates the minimum number of inference samples required to achieve a target confidence level.

  ML System DesignTechnical ScreenLLMInferenceStatistics
• 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
• MicrosoftMedium·Data Scientist
  Design Product-Description Content Moderation at Marketplace Scale

  Design an ML moderation system for multilingual seller descriptions at marketplace scale. Cover policy-aware labels, severity-based actions, calibrated thresholds, human review and appeals, low-latency serving, selective rescans, auditability, drift, and adversarial abuse.

  ML System DesignTechnical Screen
• MillenniumMedium·Data Scientist
  Design a Governed Agent for Portfolio Rebalancing

  Design an autonomous, LLM- or ML-driven portfolio rebalancing agent integrated with deterministic guardrails, pre-trade compliance checks, and audit logging. The design must guarantee safety, idempotency, strict separation of concerns, and full state reconstructibility.

  ML System DesignTechnical Screenml-system-designai-agentsrisk-management
• TTowerHard·Data Scientist
  Debug Oscillating GPU Utilization During Training

  Design a systematic debugging and architectural remediation strategy for a distributed deep learning training cluster exhibiting severe GPU utilization oscillation between 90% and 20%.

  ML System DesignTechnical Screendistributed-trainingml-system-designgpu-optimization
• Two SigmaHard·Data Scientist
  Predict Citi Bike Demand at a Specific NYC Station

  This question evaluates time-series forecasting, demand estimation, and ML system-design competencies, focusing on problem framing, handling censored observations, data integration, model selection, and evaluation under operational constraints.

  ML System DesignTechnical Screen
• Two SigmaHard·Data Scientist
  Predicting Stock Prices from Twitter Data

  This question evaluates competency in ML system design for integrating NLP-derived signals from social media with time-series financial modeling, including feature extraction, timestamp alignment, and handling low signal-to-noise ratios.

  ML System DesignTechnical Screen