Data Science Interview Guide
Data science interviews combine statistical reasoning, coding proficiency, and business analysis. This guide covers the technical areas assessed in data science interview processes.
Interview Components
| Component | Assessment Focus | Format |
|---|---|---|
| Statistics & Probability | Reasoning about uncertainty | Q&A, whiteboard |
| SQL/Coding | Data manipulation capability | Live coding |
| Machine Learning | Algorithm selection and trade-offs | Case discussion |
| Experimentation | A/B test design and analysis | Case study |
| Product Analytics | Business problem solving | Open discussion |
Technical Foundations
Probability
Covers conditional probability, Bayes' theorem, and probability distributions. The medical test problem (calculating true positive rate given test results) appears frequently.
Statistics
Covers hypothesis testing, confidence intervals, and p-value interpretation. Understanding the conceptual meaning, not just the formulas, matters.
Machine Learning
Covers algorithm selection criteria (logistic regression vs random forest vs XGBoost), bias-variance trade-off, and handling imbalanced datasets.
SQL
Covers window functions, CTEs, and aggregations. SQL appears in most interview rounds.
Product & Experimentation
Experimentation
Covers experiment design principles and common pitfalls.
A/B Testing
Covers sample size calculation, power analysis, and the statistical consequences of early result inspection.
Metrics Design
Covers metric selection criteria. Choosing the appropriate metric is foundational to correct analysis.
Core Competencies
| Competency | Assessment Rationale |
|---|---|
| Statistical intuition | Decisions are made with incomplete data |
| SQL proficiency | Majority of work involves data extraction and transformation |
| ML fundamentals | Emphasis on when to apply algorithms, not implementation details |
| Business sense | Analysis must address the relevant business question |
| Communication | Statistical concepts must be explained to non-technical stakeholders |
Preparation Approach
| Area | Recommendation |
|---|---|
| SQL | Practice daily; window functions are commonly challenging |
| Bayes' theorem | Medical test problem (positive result, rare disease) appears frequently |
| ML trade-offs | Focus on when to select each algorithm, not just how they work |
| Case studies | Practice explaining metric investigations verbally; have a structured framework |
| Experience examples | Prepare 2-3 examples of data-driven decisions |