Meta Data Scientist Interview Questions (2026)
200 real Data Scientist interview questions compiled for Meta, 200 of them tailored to Meta's actual interview flavor. Turn data into insight and models that inform decisions and products. Below: the interview process, the questions with answer outlines, the topics tested, and how to prepare.
Speed-focused loop famous for expecting two coding problems solved per 45-minute round with near-bug-free code and no compiler, using internally nicknamed round types (coding 'Ninja', design 'Pirate', behavioral 'Jedi'); team matching happens only after you pass.
Questions
200
200 company-tailored
Difficulty
Medium
from our question mix
Rounds
5
typical loop
Meta rating
4.13/5
Top 99% in industry
Meta's interview process
- 1Recruiter screen30 minEasy
Process overview, level calibration, and prep guidance — Meta recruiters actively coach on round formats.
- 2Technical screen45 minHard
Two DSA problems in 45 minutes on a plain shared editor with no autocomplete or execution.
- 3Coding round ('Ninja')45 minHard
Two more problems at loop difficulty; clean near-compilable code and verbalized complexity analysis expected.
- 4System design ('Pirate')45 minHard
Design a Meta-scale product system (feed, Stories, chat) with emphasis on read-heavy fan-out, caching, and data modeling.
- 5Behavioral ('Jedi')45 minMedium
Deep past-experience discussion on conflict, growth, and impact aligned to Meta values; graded as a real signal round.
Data Scientist interview questions asked at Meta
- Q1
Design an A/B test for a change to Facebook Feed intended to improve daily active users
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: Facebook Feed; context = ads-supported social engagement; objective = daily active users; ecosystem/entity = creators and viewers.
How to answer: Define hypothesis, treatment/control, eligible users, randomization unit, metrics, duration, and decision rule.
- Q2
Choose primary, secondary, and guardrail metrics for a Instagram Reels experiment
EasyExperiment design caseExperimentationMeta-specificContext: Meta variation: Instagram Reels; context = ads-supported social engagement; objective = watch time; ecosystem/entity = creators and viewers.
How to answer: Map metrics to business/user value; include quality, safety, latency, monetization, and retention guardrails.
- Q3
Estimate experiment duration for a WhatsApp Channels test with limited traffic and small expected lift in messages sent
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: WhatsApp Channels; context = ads-supported social engagement; objective = messages sent; ecosystem/entity = creators and viewers.
How to answer: Use baseline variance, traffic, MDE, alpha/power, seasonality, and segment prioritization.
- Q4
Should the Messenger test randomize by user, session, market, or cluster? Justify your choice
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: Messenger; context = ads-supported social engagement; objective = messages sent; ecosystem/entity = creators and viewers.
How to answer: Discuss exposure consistency, interference, statistical power, implementation, and estimand.
- Q5
Threads has social, marketplace, or ranking spillovers. How would interference violate standard A/B assumptions?
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: Threads; context = ads-supported social engagement; objective = 7-day retention; ecosystem/entity = creators and viewers.
How to answer: Explain SUTVA violations, cluster tests, switchbacks, geo tests, and spillover measurement.
- Q6
Design a switchback experiment for Meta Ads where marketplace conditions change quickly
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: Meta Ads; context = ads-supported social engagement; objective = ad click-through rate; ecosystem/entity = creators and viewers.
How to answer: Choose time windows, washout, balance, seasonality controls, clustered inference, and operational feasibility.
- Q7
Your Marketplace experiment spans a holiday or major event. How would you protect inference for buyer-seller conversion rate?
EasyExperiment design caseExperimentationMeta-specificContext: Meta variation: Marketplace; context = ads-supported social engagement; objective = buyer-seller conversion rate; ecosystem/entity = creators and viewers.
How to answer: Use pre-planned duration, stratification, time fixed effects, holdouts, and sensitivity analysis.
- Q8
Initial creator recommendations results are strong but fade after one week. How do you detect and account for novelty effects?
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: creator recommendations; context = ads-supported social engagement; objective = creator follows; ecosystem/entity = creators and viewers.
How to answer: Analyze time-since-exposure, retention cohorts, longer tests, ramp patterns, and long-term holdouts.
- Q9
The notifications test is neutral overall but positive for new users. How would you evaluate the segment claim?
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: notifications; context = ads-supported social engagement; objective = notification opt-out rate; ecosystem/entity = creators and viewers.
How to answer: Use pre-specified segments, interaction tests, power, shrinkage, and replication.
- Q10
A video ranking experiment expected 50/50 allocation but shows 53/47. How do you investigate?
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: video ranking; context = ads-supported social engagement; objective = report rate; ecosystem/entity = creators and viewers.
How to answer: Check assignment logs, eligibility, bots, logging loss, bucketing changes, and rerun criteria.
- Q11
Create a ramp plan for launching Facebook Feed after a positive experiment on daily active users
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: Facebook Feed; context = ads-supported social engagement; objective = daily active users; ecosystem/entity = creators and viewers.
How to answer: Use phased traffic, guardrail monitoring, segment checks, incident thresholds, and owner accountability.
- Q12
Before testing Instagram Reels, what instrumentation and logging validation would you require?
EasyExperiment design caseExperimentationMeta-specificContext: Meta variation: Instagram Reels; context = ads-supported social engagement; objective = watch time; ecosystem/entity = creators and viewers.
How to answer: Validate exposure, assignment, event timing, metric definitions, missingness, and audit dashboards.
- Q13
Another team is testing a related change that may affect WhatsApp Channels. How would you handle concurrent experiments?
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: WhatsApp Channels; context = ads-supported social engagement; objective = messages sent; ecosystem/entity = creators and viewers.
How to answer: Discuss interaction effects, layer design, orthogonal allocation, exclusions, and analysis adjustments.
- Q14
A PM wants to stop the Messenger test as soon as messages sent becomes significant. What do you advise?
MediumExperiment design caseExperimentationMeta-specificContext: Meta variation: Messenger; context = ads-supported social engagement; objective = messages sent; ecosystem/entity = creators and viewers.
How to answer: Explain inflated false positives, pre-set analysis windows, alpha spending, and decision governance.
- Q15
Only some users actually see Threads. Should you analyze all assigned users or only exposed users?
HardExperiment design caseExperimentationMeta-specificContext: Meta variation: Threads; context = ads-supported social engagement; objective = 7-day retention; ecosystem/entity = creators and viewers.
How to answer: Contrast ITT, treatment-on-treated, triggering, compliance, and unbiased estimators.
Practice these with instant AI feedback in a live mock interview → Start a Meta Data Scientist mock
Topics tested most
How to prepare for the Meta Data Scientist interview
Be fast and correct on coding; for design, drive the conversation; prepare impact-focused behavioral stories
Indicative Data Scientist pay in India: ~₹11–48 LPA (role-level range, not a Meta-specific figure).
Frequently asked questions
How hard is the Meta Data Scientist interview?
Based on our bank of 200 Data Scientist questions asked at Meta, the overall difficulty is medium (Meta's process is generally rated extreme). Expect around 5 rounds spanning Machine Learning, Statistics, Experimentation.
How many interview rounds does Meta have for a Data Scientist?
Meta typically runs about 5 rounds for Data Scientist candidates: Recruiter screen → Technical screen → Coding round ('Ninja') → System design ('Pirate') → Behavioral ('Jedi').
What is the interview process at Meta?
The Meta interview process typically runs: Recruiter screen -> technical screen -> onsite (coding x2, system/product design, behavioral 'Jedi'). Prepare for each round in order rather than only the first — the later stages usually carry the most weight.
How hard is the Meta interview?
Meta interviews are rated very high difficulty. The bar is highest on coding speed & accuracy — go deep there and practise explaining your reasoning out loud.
What does Meta look for in candidates?
Meta focuses on Coding speed & accuracy, system/product design, behavioral signal. Culturally, it values Move fast, be bold, focus on impact, be open. Line up your examples to hit both the technical bar and these values.
Explore more
Other roles at Meta
Data Scientist interviews at other companies
Compiled by PrepNPlaced from 200+ interview reports and question banks for the Meta Data Scientist loop, cross-referenced with 75 employee reviews. Data refreshed 2026-07-12. Updated 2026.