Dataford
Dataford
Top 38
Prep plan
Updated weekly · Last refresh Oct 17

Google DeepMind Data Scientist Interview Questions

The questions to prepare for a Google DeepMind Data Scientist interview. Questions from real interview reports rank first. Updated daily.

38questions
~5htotal time
Track your progressSign up free to work through all 38 questions and resume where you left off.
Start practicing free →
1
A/B Testing & ExperimentationStart here. 4 questions · ~34 min
Network Interference in Collaboration TestMedium

Explain when network interference threatens an A/B test, how it biases estimates, and how to redesign the experiment safely.

Network InterferenceExperimentationA/B TestingGoogle DeepMind
More A/B Testing & Experimentation questions with a free account
2
Model Evaluation3 questions · ~26 min
Explain Precision Recall TradeoffEasy

Explain precision versus recall in plain language and how the tradeoff affects product decisions.

PrecisionThreshold TuningRecallGoogle DeepMind
More Model Evaluation questions with a free account
3
Machine Learning9 questions · ~77 min
Bias Variance and RegularizationMedium

Explain the bias-variance tradeoff mathematically and how L1 and L2 regularization change model complexity and weights.

Bias-Variance TradeoffRegularizationSupervised LearningGoogle DeepMind
More Machine Learning questions with a free account
4
Metrics3 questions · ~26 min
More Metrics questions with a free account
5
Behavioral & Leadership10 questions · ~85 min
More Behavioral & Leadership questions with a free account
6
More topics9 questions · ~77 min
Window Functions for Running TotalsMedium
Practice

Use PostgreSQL window functions to calculate user running spend and rank users by total spend within each signup cohort.

Window FunctionsRankingRunning TotalsGoogle DeepMind
Sample Size for A/B TestsEasy

Choose sample size and runtime by combining baseline rate, MDE, alpha, power, and expected traffic.

Power AnalysisSample SizeA/B TestingGoogle DeepMind
Design a Cold Start RankerMedium

Design a recommendation and ranking system that handles cold start for both new users and new items without hurting feed quality.

Cold StartTwo-Tower ModelsRecommendation SystemsGoogle DeepMind
Define Feature Success MetricsMedium

Framework for choosing a feature's primary success metric and guardrails before launch.

MetricsFeature PrioritizationProduct VisionGoogle DeepMind
Batch vs Streaming Data ProcessingEasy

Compare batch and streaming data processing, including when each fits best in a pipeline.

Stream ProcessingETLBatch ProcessingGoogle DeepMind
More questions with a free account

Sign up to see every question

Create a free account to unlock this list and practice real interview questions.

Get my prep plan
Hands-on SQL practiceWrite and run real queries in the editor. 2 drills · ~20 min
Top Marketing Channels with Window FunctionsMedium
Practice
Practice drill

Rank monthly marketing channels by ROAS and compare each channel with its prior month using advanced window functions.

Window FunctionsData ManipulationRankingGoogle DeepMind
SQL Conversion Rate by CampaignEasy
Practice
Practice drill

Calculate daily campaign conversion rates with conditional aggregation, a CTE, and a campaign dimension join.

Date FunctionsGroup ByAggregationsNianticBrazeOutschool
The finish line: interview-readyComplete all 38 questions plus 2 hands-on drills to finish this plan.