This is a breakdown of data science interviews and how to prepare for them.
This guide focuses on what interviewers are looking for: not just technical correctness, but your ability to reason through ambiguity, communicate clearly, and tie your work to business outcomes.
By the end of this guide, you’ll have a high-level framework for approaching every round with clarity, confidence, and structure.
Not all data science interviews are created equal.
The questions you’ll face and the skills you need to highlight depend heavily on the specific flavor of data science role you’re targeting.
First, understand what kind of data scientist the company is hiring for.
Role | Focus | Skills | Examples |
---|---|---|---|
Machine Learning | Building and tuning ML models and systems. | - ML systems at scale - Algorithm tuning - Model vs. business performance |
Airbnb: Build scalable ML models and pipelines NVIDIA: Implement algorithms for large-scale projects Thumbtack: Deploy ML systems TikTok: AI/ML including NLP, CV, audio processing |
Product Analytics | Driving business decisions and experiments using data | - SQL - Product sense - Business metrics - Experimentation |
Doordash: Build analytics experiments and dashboards Meta: Measure product success with metrics Waymo: Track health of commercial products and run experiments |
Full Stack | Combination of ML, product, and statistical analysis | - Causal inference - EDA - Statistical models - Hypothesis testing |
Walmart: Design interventions with statistical methods Grammarly: Run SEO experiments with causal inference Google: Apply statistical methods to product development |
Engineering | Preparing and processing large-scale data for others | - Big data tech (Spark) - Batch pipelines - Scala/Python |
Netflix: Build systems to process and model data LinkedIn: Build performant systems for massive-scale analysis |
Here are the four most common role types:
These roles expect you to design, tune, and sometimes productionize ML models.
You'll see fewer business metric questions and more deep dives into algorithms, pipelines, and model evaluation.
Interview focus:
These are closer to product manager or business analyst roles, focusing on generating insights, influencing decisions, and driving product growth through data.
Interview focus:
These roles require strong ML chops and a solid business and product strategy.
You’re expected to own projects end-to-end, from defining metrics to deploying models and analyzing impact.
Interview focus:
Not a traditional DS role, but some job titles overlap. Data engineering roles are more focused on infrastructure, pipelines, and tooling.
Interview focus:
Read the job description closely.
Your prep should lean analytical if it emphasizes A/B tests, SQL, and metrics. If it calls for building pipelines and tuning models, go deeper on ML and systems.
While the exact process varies by company and role type, here’s a typical breakdown of what to expect:
Approximately 30 minutes.
This is a quick fit check. The recruiter will:
Approximately 30-60 minutes.
You’ll face 2–4 short questions, usually around:
Approximately 60 minutes.
It is one of the most common and heavily weighted rounds for analytics and product-focused roles.
You may be asked to:
Approximately 60 minutes.
The SQL round tests your ability to manipulate data directly, often from 1–2 tables with joins, filters, and aggregations.
Expect to:
GROUP BY
, WINDOW FUNCTIONS
, CASE
Approximately 60 minutes.
You’ll be asked to code up and evaluate a small ML model, typically in Python.
Think of real-world scenarios like churn prediction, fraud detection, or personalization.
Approximately 60 minutes.
This round explores your understanding of key ML algorithms and trade-offs. (e.g., linear regression, decision trees, KNN)
Common questions:
Approximately 45-60 minutes.
Primarily for analytics-focused roles, these rounds mimic the product management interview. You’ll be expected to:
Read more: Case Study Interviews for Data Scientists
Approximately 30-60 minutes.
Data science behavioral rounds test collaboration, leadership, and how you communicate technical work.
Expect questions like:
📌 Prep tip: Use a consistent story format (e.g. STAR), but tailor stories to the company’s values and goals.
Approximately 2-5 hours.
Take-home assignments are common at startups or early-stage teams.
You’ll be asked to analyze a dataset and present findings. Sometimes open-ended (“Find something interesting”), other times structured.
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