Interviews Analyzed
3,200
What data scientists are actually asked in interviews.
Analysis of 3,200+ data scientist interview reports from candidates at Meta, Google, Netflix, and 150+ other companies.
Average Prep Time
12weeks
foundations
deep
system
polish
Offers Landed
72%
Among candidates following the plan
Avg Salary Bump
+$38k
Pre-offer vs post-offer base + equity
We pull jobs from sources most job seekers never check
01 — Companies
What top companies emphasize.
Interview focus varies significantly across company types — from algorithmic depth at FAANG to business impact at startups.
FAANG
HighMeta · Google · Amazon · Apple · Netflix
100%
- Algorithms 35%
- System design 30%
- Behavioral 20%
- Domain / fit 15%
Heavy emphasis on ML theory, statistical rigor, and system design for large-scale data processing.
ML TheoryScale
FINTECH
HighStripe · Square · Robinhood · Plaid · Affirm
100%
- Algorithms 25%
- System design 25%
- Behavioral 25%
- Domain / fit 25%
Focus on risk modeling, fraud detection, and regulatory compliance with strong statistical foundations.
Risk ModelingCompliance
EARLY-STAGE · SERIES A-B
MediumVarious startups · scale-ups
100%
- Algorithms 20%
- System design 15%
- Behavioral 25%
- Domain / fit 40%
Emphasis on business impact, scrappy problem-solving, and ability to wear multiple hats.
Business ImpactGeneralist
02 — Topics
Most frequently tested topics
67% of interviews containing topic
01
Statistics & Probability
85%hypothesis testing, distributions, confidence intervals, p-values, bayesian
Fundamental statistical concepts appear in most data science interviews
02
Machine Learning
78%supervised learning, unsupervised, model evaluation, overfitting, cross-validation
Core ML algorithms and evaluation methods are heavily tested
03
SQL & Data Manipulation
72%joins, window functions, aggregations, subqueries, pandas
Data querying and manipulation skills are essential for most roles
04
Python Programming
68%data structures, algorithms, pandas, numpy, debugging
Programming fundamentals and data science libraries
05
A/B Testing & Experimentation
58%experimental design, statistical significance, power analysis, bias, causal inference
Product experimentation methodology is crucial for product-focused roles
06
Business Case Studies
45%metrics, business impact, stakeholder communication, prioritization, trade-offs
Translating data insights into business recommendations
03 — Interview loop
Typical interview process
ML system design often becomes the bottleneck — candidates struggle with scaling data pipelines and model deployment architecture.
Pass-rate funnel
Phone Screen · 78%
Technical Coding · 65%
ML System Design · 42%
Case Study · 58%
ML Theory · 52%
Behavioral · 68%
Offer rate compounded ≈ 1.3%
01
Phone Screen
45 min · pass 78%
Basic statistics, SQL, and Python fundamentals
02
Technical Coding
60 min · pass 65%
Data manipulation, algorithms, and statistical programming
03
ML System Design
BOTTLENECK60 min · pass 42%
Design end-to-end ML systems and data pipelines
04
Case Study
60 min · pass 58%
Business problem solving with data analysis
05
ML Theory
45 min · pass 52%
Deep dive into algorithms, statistics, and model evaluation
06
Behavioral
45 min · pass 68%
Leadership, collaboration, and past project discussions
04 — Question bank
Real questions you'll encounter.
Curated from actual data scientist interviews at top companies.
STATISTICS
Medium → HardA/B test analysis
- statistical significance
- power analysis
- multiple testing
- confidence intervals
MACHINE LEARNING
Medium → HardModel evaluation
- bias-variance tradeoff
- cross-validation
- overfitting detection
- feature selection
SQL
MediumComplex joins
- window functions
- recursive queries
- data aggregation
- performance optimization
PYTHON
Easy → MediumData manipulation
- pandas operations
- data cleaning
- missing values
- groupby operations
SYSTEM DESIGN
HardRecommendation system
- collaborative filtering
- content-based
- cold start problem
- scalability
BUSINESS CASE
MediumMetric design
- KPI selection
- business impact
- trade-off analysis
- stakeholder alignment
850 questions in the bank
Open the full bank →05 — Prep roadmap
12-week preparation roadmap
Structured path from statistics fundamentals to advanced ML system design, with increasing weekly time commitment.
Hours / week
Total: 78 hrs
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
Weeks 1-3
4 hrs/wk
Statistics & Python Foundations
Build core statistical knowledge and Python programming skills essential for data science interviews.
StatisticsPythonPandasProbability
Weeks 4-7
6 hrs/wk
Machine Learning Deep Dive
Master ML algorithms, model evaluation, and feature engineering techniques commonly tested in interviews.
Supervised LearningModel EvaluationFeature EngineeringCross-validation
Weeks 8-10
8 hrs/wk
System Design & Scale
Learn to design end-to-end ML systems, data pipelines, and handle large-scale data processing challenges.
ML SystemsData PipelinesScalabilityArchitecture
Weeks 11-12
7 hrs/wk
Interview Practice & Polish
Practice case studies, mock interviews, and refine communication of technical concepts to non-technical stakeholders.
Case StudiesCommunicationMock InterviewsBusiness Impact
06 — Tools & resources
Tools & resources that work.
Battle-tested by candidates who landed offers.
Mix of free + premium.
InterviewPal
Guided interview prep with mentorship and structured paths.
Best for: Structured prep
Visit InterviewPal
LeetCode
2,000+ coding problems. Premium unlocks company-tagged sets.
Best for: Algorithms & DS
Visit LeetCode
System Design Primer
Free comprehensive guide. The de-facto starting point.
Best for: SD fundamentals
Visit System Design Primer
Blind
Anonymous tech community. Real interview experiences and insights.
Best for: Real signal
Visit Blind
Levels.fyi
Salary and interview data, by company and level.
Best for: Company intel
Visit Levels.fyi
Pramp
Peer mock interviews. Live practice with real people.
Best for: Live practice
Visit PrampFrequently Asked Questions
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