Twitch Interview Questions — Page 2

Twitch·Product Manager·Hiring Manager Screen

Jun 2026

Short PM screen at Twitch, basically just one question about motivation for the role.

Why do you want to work at Twitch?

“Pretty standard opener but I fumbled it a bit.”

Twitch·Data Scientist·Technical Phone Screen

Jun 2026

SQL-heavy technical screen for a Data Scientist role at Twitch, all revolving around two streaming tables. Four questions, each building on the last, and the multi-year edge case stuff caught me more off guard than I'd like to admit.

Using the minute_streamed table, write a query that returns total hours streamed for each calendar month, ordered chronologically. Also explain how your query handles data that spans multiple years.
For each streamer, compute their total streamed hours and the percentage of those hours spent streaming content in a category that matches a given keyword. The match should be case-insensitive.
Identify all streamers whose total streamed hours in a given month exceeded their total from the prior month. Your solution needs to handle multiple years of data and months where a streamer had no activity at all.
Join minute_streamed and minute_viewed to produce, per streamer, their average concurrent viewers in 2019 and the total minutes watched by viewers from the US in 2019.

“Went straight to COUNT(*)/60 grouped by month and forgot to include year in the group-by key.”

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Twitch·Data Scientist·Technical Phone Screen

Jul 2026

Twitch data scientist interview with a heavy SQL focus. Four problems, all streaming analytics, all nastier than they look on paper. The schema is clean but the edge cases are where they actually test you.

Given a streaming platform schema with view start/stop events, write a SQL query to find peak concurrent viewers per stream and the time window where that peak occurs. Events may arrive out of order and exact duplicate rows must be deduplicated.
For users who watched multiple different streams simultaneously on a given date, compute the total minutes each user spent watching two streams concurrently, but only count overlap windows of at least 5 minutes.
Compute D+7 retention for users whose first-ever view was in a specified date range. A user is retained if they watch any stream for at least 2 continuous minutes on exactly their 7th day after first view.
Rank creators by average per-viewer watch time over a 7-day window, restricted to US viewers. Only include creators with at least 100 unique US viewers. Break ties using peak concurrency from the earlier query.

“This one wrecked me more than I expected.”

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Twitch·Data Scientist·Onsite - System Design / Architecture

Jul 2026

Twitch data scientist system design round, focused entirely on building a real-time recommendation system for live streams. It was a deep, multi-part question and I felt like I was sprinting the whole time.

How would you define the objective and construct training labels for a real-time live stream recommendation system, including handling delayed outcomes and streams that end mid-session?
What features would you use across user, creator, stream, and context dimensions, and how do you handle cold-start for brand new creators or users?
Walk through your model architecture choice, loss function, negative sampling strategy, and how you'd deal with severe class imbalance in this setting.
How would you detect and mitigate position bias and popularity bias in your training data, and what exploration policy would you use?
What offline and online metrics would you track, and how would you validate that offline metric improvements actually translate to online gains?
Describe the end-to-end serving architecture for this system with a sub-100ms p95 latency budget, including how you'd keep embeddings and stream availability signals fresh in near-real time.

“This is where I fumbled a bit at the start.”

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Twitch·Data Scientist·Technical Phone Screen

Jul 2026

Twitch data scientist interview that was basically a full SQL gauntlet built around streaming telemetry data. Five tasks back to back, ranging from monthly aggregations to window functions to a conceptual question about query processing order. Felt more like a take-home than a live screen.

Given minute-level streaming data, compute total monthly hours streamed across all streamers. Output should be labeled as YYYY-MM and ordered chronologically, and must handle data spanning multiple years correctly.
For each streamer, compute their total streamed minutes and the share of minutes spent in categories matching a given keyword (case-insensitive substring match). Return the keyword share as a ratio.
For each streamer and calendar month, determine whether their streamed hours increased compared to the immediately preceding calendar month. Treat months with no activity as zero. Only return rows where current month exceeds the previous month.
For 2019 only, return each streamer's average concurrent viewers and total US viewer-minutes. Join minute_viewed to minute_streamed on both streamer and timestamp to avoid row multiplication. As a follow-up, also count distinct streamed minutes where at least one US viewer was present.
Explain SQL's logical query processing order and clarify whether AVG() is computed before or after the JOIN.

“The year-boundary thing is what trips people up.”

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