Interview Prep
Inside the Meta Data Scientist Interview in 2026: SQL, Product Analytics & a Real Technical Screen
Qian Zhou · Marketing Specialist ·
Quick Answer
- Meta’s public Data Scientist guide describes the initial interview as a 45-minute conversation combining analytical and technical components. Candidates should confirm later stages with their recruiter.
- For Data Scientist / Product Analytics candidates, prioritize practical SQL, product metrics, statistics, experimentation, and clear assumptions.
- One recent intermediate-level candidate reported two SQL questions in a technical phone screen. The key traps were aggregation level, inclusive dates, and integer division.
- This single case is not a universal Meta question set; it is evidence of the precision and communication a technical screen may test.
URL:https://www.screna.ai/experience/63ffb40c-fd9b-4f90-8f78-1c5062b13ee5
Meta Data Scientist Interview Snapshot
| Company | Meta |
|---|---|
| Exact role covered | Data Scientist |
| Level | Intermediate |
| Reported round | Technical Phone Screen |
| Case focus | Seller-product aggregation and category interaction share |
| Core signals | Query structure, data grain, date boundaries, numerical correctness, communication |
How the Initial Screen Is Framed
A correct query is only part of the task. Candidates may need to translate product language into data logic, define the right unit of analysis, explain assumptions, and make the query easy to follow in real time.
Real Candidate Case: Two SQL Questions, Three Traps
Question 1 — Multi-level aggregation. The interviewer asked how many sellers had more than three distinct products where each seller-product pair had total interactions greater than one.
The candidate initially treated “multiple interactions” as row count rather than the sum of the interaction field. After re-reading the prompt, the candidate corrected the sequence: aggregate by seller_id + product_id, filter pairs with summed interactions above one, count qualifying distinct products per seller, filter sellers above three products, then count those sellers.
“The definition of ‘multiple interactions’ tripped me up at first.” — Candidate
Screna AI mentor Jamie_Clicks recommends CTEs so the interviewer can track the two aggregation levels. This may indicate that readable decomposition matters when a prompt mixes data grain and thresholds.
“You have to fully resolve the seller-product aggregation before you can meaningfully filter on product count per seller.” — Jamie_Clicks, Screna AI Mentor
Question 2 — Percentage, dates, and safe arithmetic. The interviewer asked for the percentage of total listing interactions from the vehicle category among products created in the US within the last seven days, returned from 0 to 100.
The candidate caught two details after starting: an inclusive seven-day window means today plus the previous six days, and numerator and denominator must both use summed interactions. The candidate also nearly used integer division.
Screna AI mentor Marcus Thorne recommended 100.0 or a float cast plus a conditional SUM for the vehicle numerator. A query can look correct yet return the wrong percentage if integer division truncates it; country and creation-date filters should be applied through the product dimension before aggregation.
What These Questions May Be Evaluating
| Data grain | Identify seller, product, seller-product pair, or interaction row before coding. |
|---|---|
| Aggregation precision | Distinguish row counts from measures that must be summed. |
| Boundary logic | Treat wording such as “last seven days inclusive” as part of the computation. |
| Communication | Narrate each aggregation and filter so the interviewer can follow the reasoning. |
The broader Meta DS framework also emphasizes product metrics, statistics, experimentation, and recommendations. Strong responses should connect technical correctness to product meaning and decisions.
A Focused Preparation Plan
Practice SQL that aggregates at one grain, filters, then re-aggregates. Cover joins, conditional aggregation, dates, NULL handling, safe percentages, and edge cases. For product analytics, use: Define -> Segment -> Hypothesize -> Test -> Recommend. Add drills around bias, confounders, interpretation, and decisions.