Meta Data Scientist Interview Guide

Most candidates prepping for Meta's Data Scientist, Finance role assume the interview is a statistics and SQL gauntlet. It's not. Roughly 45% of the evaluation centers on product sense and business acumen, and the Finance Analytics specialization adds its own wrinkle: you need to talk fluently about ROI modeling, revenue forecasting, and infrastructure investment decisions, not just engagement metrics.

Meta Data Scientist Role

Meta's Finance Analytics DS sits inside the finance org rather than a consumer product team, which changes the texture of the work. You're building forecasting models for ad revenue, sizing the return on data center investments, and doing capacity planning that feeds directly into capital allocation decisions. Success after year one means your analysis changed how a finance leader allocated budget or prioritized infrastructure spend, not just that you delivered a clean deck.

A Typical Week

The surprise isn't the coding time. It's how much of your week goes to writing structured findings docs and aligning with cross-functional partners before you ever present. Tuesdays and Wednesdays are your narrow window for deep analytical work in Daiquery and Bento notebooks, so protect those blocks aggressively or they'll vanish into ad-hoc requests.

Projects & Impact Areas

Ad revenue forecasting anchors the Finance DS workstream because Meta's planning cycles depend on accurate forward-looking models, and even small forecast errors compound into billion-dollar misallocations. That forecasting work connects directly to capacity planning for AI infrastructure buildout, where you're modeling whether the next tranche of GPU clusters will pay for itself through improved Advantage+ campaign performance. Causal inference shows up here too: measuring the true incremental ROI of infrastructure capex requires more than correlation, and finance leadership expects rigorous counterfactual analysis before signing off on spend.

Skills & What's Expected

Business acumen is the only skill rated "expert" for this role, sitting above even statistics and math (rated "high"). SQL and Python proficiency are table stakes for reaching the onsite, but they won't separate you from other candidates. The differentiator is whether you can take an ambiguous finance question (like "should we accelerate data center buildout in Q3?") and decompose it into a measurable framework that a VP can act on. Communication skills matter because your readouts go to senior finance leaders, not just your immediate pod.

Levels & Career Growth

IC4 is the most common entry point for experienced hires. The jump from IC5 to IC6 is where most careers stall, and the blocker at Meta specifically is demonstrating influence beyond your immediate team: shaping the Finance Analytics roadmap, mentoring IC3/IC4 scientists, and getting senior leadership to adopt your frameworks org-wide. Shipping excellent analyses alone won't clear that bar.

Work Culture

From what candidates and current employees report, Meta requires three days in-office per week (Tuesday through Thursday is the common pattern), and remote DS positions are increasingly scarce. DSs in Finance Analytics have real authority to challenge assumptions in planning models and push back when the numbers don't support a proposed investment. You own your analyses end-to-end, from the SQL query to the final recommendation, which means autonomy but also full accountability when a forecast misses.

Meta Data Scientist Compensation

Quarterly RSU vesting changes your Year 1 cash flow more than most candidates realize. Because you're receiving equity income every quarter rather than waiting for a large annual chunk, your take-home pay feels meaningfully higher from day one. Refresh grants at IC5 and above can also outpace the annualized value of your initial grant after a strong performance cycle, so the offer letter you sign is really just the floor.

The single biggest negotiation lever at Meta is leveling, not base salary. The comp widget shows a massive gap between adjacent levels, and from what candidates report, the RSU and sign-on components have more room to move than base. If you're on the border between two levels, spend your negotiation energy pushing for the higher one. Everything else is rounding error by comparison.

Meta Data Scientist Interview Process

Budget about six weeks from first call to final decision, though onsite scheduling can push it to eight. The recruiter screen covers your background, motivations, and career fit, not technical problems, but it's where Meta filters out candidates who can't connect their past work to measurable business outcomes on products like Ads or Reels. Rejection reasons span the full spectrum, from weak SQL to poor statistical foundations to lack of product intuition, so over-indexing prep on any single area is a mistake.

From what candidates report, team matching to a specific org (Ads, Instagram, Reality Labs) tends to happen after the hiring committee makes its decision, not before. Keep your product sense answers broad enough to signal flexibility across Meta's product portfolio. Your offer timeline may go quiet for a week between approval and placement, which is normal and not a sign something went wrong.

Meta Data Scientist Interview Questions

1WITH
2params AS (
3  SELECT
4    DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AS start_date,
5    CURRENT_DATE() AS end_date
6),
7
8-- Impression events (one row per impression)
9impressions AS (
10  SELECT
11    advertiser_id,
12    impression_id,
13    user_id,
14    ad_id,
15    impression_ts,
16    DATE(impression_ts) AS impression_date
17  FROM ads_impressions
18  WHERE DATE(impression_ts) BETWEEN (SELECT start_date FROM params) AND (SELECT end_date FROM params)
19),
20
21-- Conversion payments (one row per conversion)
22conversions AS (
23  SELECT
24    advertiser_id,
25    conversion_id,
26    user_id,
27    conversion_ts,
28    DATE(conversion_ts) AS conversion_date,
29    revenue_usd
30  FROM payments_conversions
31  WHERE DATE(conversion_ts) BETWEEN (SELECT start_date FROM params) AND (SELECT end_date FROM params)
32),
33
34-- Attribute each conversion to the most recent eligible impression within 7 days.
35-- This is where most people fail: if you join impressions to conversions without ranking,
36-- you duplicate revenue for users with many impressions.
37attributed AS (
38  SELECT
39    c.advertiser_id,
40    c.conversion_id,
41    c.revenue_usd,
42    c.conversion_ts,
43    i.impression_ts,
44    DATE(i.impression_ts) AS attributed_impression_date,
45    ROW_NUMBER() OVER (
46      PARTITION BY c.advertiser_id, c.conversion_id
47      ORDER BY i.impression_ts DESC
48    ) AS rn
49  FROM conversions c
50  JOIN impressions i
51    ON i.advertiser_id = c.advertiser_id
52   AND i.user_id = c.user_id
53   AND i.impression_ts <= c.conversion_ts
54   AND i.impression_ts >= TIMESTAMP_SUB(c.conversion_ts, INTERVAL 7 DAY)
55),
56
57attributed_dedup AS (
58  SELECT
59    advertiser_id,
60    conversion_id,
61    revenue_usd,
62    attributed_impression_date
63  FROM attributed
64  WHERE rn = 1
65),
66
67revenue_by_day AS (
68  SELECT
69    advertiser_id,
70    attributed_impression_date AS ds,
71    SUM(revenue_usd) AS attributed_revenue_usd,
72    COUNT(DISTINCT conversion_id) AS conversions
73  FROM attributed_dedup
74  GROUP BY 1, 2
75),
76
77spend_by_day AS (
78  SELECT
79    advertiser_id,
80    spend_date AS ds,
81    SUM(spend_usd) AS spend_usd
82  FROM ads_spend_daily
83  WHERE spend_date BETWEEN (SELECT start_date FROM params) AND (SELECT end_date FROM params)
84  GROUP BY 1, 2
85)
86
87SELECT
88  COALESCE(s.advertiser_id, r.advertiser_id) AS advertiser_id,
89  COALESCE(s.ds, r.ds) AS ds,
90  COALESCE(r.attributed_revenue_usd, 0.0) AS attributed_revenue_usd,
91  COALESCE(s.spend_usd, 0.0) AS spend_usd,
92  COALESCE(r.conversions, 0) AS conversions,
93  CASE
94    WHEN COALESCE(s.spend_usd, 0.0) = 0.0 THEN NULL
95    ELSE COALESCE(r.attributed_revenue_usd, 0.0) / s.spend_usd
96  END AS roas
97FROM spend_by_day s
98FULL OUTER JOIN revenue_by_day r
99  ON r.advertiser_id = s.advertiser_id
100 AND r.ds = s.ds
101ORDER BY ds, advertiser_id;

1WITH
2params AS (
3  -- Treat this as the report run date (for example, today).
4  SELECT CURRENT_DATE() AS as_of_date
5),
6
7-- Monthly forecast at grain (region, month_start).
8forecast_monthly AS (
9  SELECT
10    region,
11    month_start_date,          -- first day of month
12    forecast_units_month       -- forecast for full month
13  FROM infra_capacity_forecast_monthly
14),
15
16-- Daily actual usage at grain (region, usage_date).
17actual_daily AS (
18  SELECT
19    region,
20    usage_date,
21    actual_units
22  FROM infra_usage_daily
23  WHERE usage_date <= (SELECT as_of_date FROM params)  -- prevents future leakage
24),
25
26actual_mtd AS (
27  SELECT
28    region,
29    DATE_TRUNC(usage_date, MONTH) AS month_start_date,
30    SUM(actual_units) AS actual_units_mtd
31  FROM actual_daily
32  GROUP BY 1, 2
33),
34
35-- Convert monthly forecast to an MTD forecast by prorating by elapsed days.
36-- Assumption: forecast is spread uniformly across days in the month.
37forecast_mtd AS (
38  SELECT
39    f.region,
40    f.month_start_date,
41    f.forecast_units_month,
42    -- elapsed days in month including as_of_date, capped to month boundaries
43    (
44      DATE_DIFF(
45        LEAST((SELECT as_of_date FROM params), LAST_DAY(f.month_start_date)),
46        f.month_start_date,
47        DAY
48      ) + 1
49    ) AS elapsed_days,
50    (DATE_DIFF(LAST_DAY(f.month_start_date), f.month_start_date, DAY) + 1) AS days_in_month
51  FROM forecast_monthly f
52  WHERE f.month_start_date <= DATE_TRUNC((SELECT as_of_date FROM params), MONTH)
53),
54
55forecast_mtd_final AS (
56  SELECT
57    region,
58    month_start_date,
59    forecast_units_month,
60    forecast_units_month * (elapsed_days * 1.0 / NULLIF(days_in_month, 0)) AS forecast_units_mtd
61  FROM forecast_mtd
62)
63
64SELECT
65  COALESCE(a.region, f.region) AS region,
66  COALESCE(a.month_start_date, f.month_start_date) AS month_start_date,
67  COALESCE(a.actual_units_mtd, 0.0) AS actual_units_mtd,
68  COALESCE(f.forecast_units_mtd, 0.0) AS forecast_units_mtd,
69  CASE
70    WHEN COALESCE(f.forecast_units_mtd, 0.0) = 0.0 THEN NULL
71    ELSE (COALESCE(a.actual_units_mtd, 0.0) - f.forecast_units_mtd) / f.forecast_units_mtd
72  END AS mtd_forecast_error
73FROM actual_mtd a
74FULL OUTER JOIN forecast_mtd_final f
75  ON a.region = f.region
76 AND a.month_start_date = f.month_start_date
77ORDER BY month_start_date, region;

What jumps out from this distribution is that SQL, the skill most candidates over-prepare, accounts for the smallest testable share, while the questions that actually decide your outcome blend product judgment with statistical rigor in a single prompt. A Meta interviewer might ask you to evaluate a price floor change's impact on advertiser spend, and you'll need to simultaneously frame the right success metric for the Ads auction, propose an identification strategy given non-random rollout, and flag how heavy-tailed advertiser revenue warps your inference. Prep accordingly: time spent on metric decomposition frameworks for Reels, Advantage+ campaigns, and ad billing flows will pay off across most of the interview, not just one round.

Practice with Meta-specific product sense and experimentation questions at datainterview.com/questions.

How to Prepare for Meta Data Scientist Interviews

Meta pulled in roughly $201B in full-year revenue, up about 24% year-over-year, and the company's public messaging ties much of that momentum to AI investments across its ads stack. On the other side of the portfolio, Reality Labs is separating Quest from Worlds and shifting Worlds to a mobile-first strategy, which means DS teams there are focused on improving how people get matched with apps and games, and building out analytics that help developers understand their audience on a still-maturing platform.

The "why Meta" answer that actually works connects these two realities: a massive, AI-accelerated ads business funding long-horizon bets where the data problems are genuinely unsolved. Instead of gesturing at Meta's mission statement, pick a concrete measurement tension. How would you build engagement metrics for a VR platform where session structure looks nothing like a mobile feed? What does "incremental conversion" even mean when an ad platform is automating targeting decisions on the advertiser's behalf? According to Meta's own analytics career blog, growth here is measured on technical depth, business impact, and leadership, so framing your answer around a hard measurement problem (not just a product you like) signals you understand what the role actually demands.

Try a Real Interview Question

1WITH spend_q AS (
2  SELECT
3    DATE_TRUNC('quarter', spend_date) AS quarter,
4    campaign_id,
5    SUM(spend_usd) AS total_spend
6  FROM campaign_daily_spend
7  GROUP BY 1, 2
8),
9revenue_q AS (
10  SELECT
11    DATE_TRUNC('quarter', revenue_date) AS quarter,
12    campaign_id,
13    SUM(revenue_usd) AS total_revenue
14  FROM campaign_daily_revenue
15  GROUP BY 1, 2
16)
17SELECT
18  s.quarter,
19  s.campaign_id,
20  s.total_spend,
21  COALESCE(r.total_revenue, 0) AS total_revenue,
22  (COALESCE(r.total_revenue, 0) - s.total_spend) * 1.0 / s.total_spend AS inc_roi
23FROM spend_q s
24LEFT JOIN revenue_q r
25  ON r.quarter = s.quarter
26 AND r.campaign_id = s.campaign_id
27WHERE s.total_spend <> 0
28ORDER BY 1, 2;

Meta's SQL rounds aren't about writing a syntactically correct query and moving on. You'll need to explain what the output means for a product decision, defend your choice of granularity, and handle ambiguity in definitions like "active user" or "session" that don't come pre-specified. Practice these patterns regularly at datainterview.com/coding.

Test Your Readiness

If any of those questions felt shaky, spend focused time on product sense and experimentation frameworks at datainterview.com/questions. Those two categories alone account for nearly half of what Meta asks.