Creator Payout Pot

Twitch: Divide a fixed monthly pot between creators according to how much they were watched.

Assignment

The creator payments team allocates a fixed pot of $40,000.00 each month, shared between creators in proportion to how much they were watched. May's payment run is due and they want the split prepared. The whole pot is allocated every month — the amount does not change with how much watching happened.

Creators who cannot be paid in a given month keep what they earned — the balance is released when their status clears.

They have shared two exports: the watch sessions recorded for May, and the creator list.

Deliverables

• Each creator's share of watch time and the amount allocated to them, adding up to the pot.
• How much of the pot is actually paid out this month, and how much is held.
• Whether the pot is spread evenly across creators or concentrated in a few.
• A short closing report for the payments team, covering the rules you applied and anything they need to decide before the run goes ahead.

Data Description

watch_sessions.csv is the watching the platform recorded for May:

• session_id
• creator_id
• viewer_id
• started_at
• minutes_watched
• is_subscriber

creators.csv has one row per creator:

• creator_id
• creator_name
• joined_on - the date the creator started streaming on the platform
• payout_status - whether the creator can be paid in this run
• status_since - the date a non-active status took effect, blank for active creators

Practicalities

You can solve this in Python, SQL, or a mix of both — StrataScratch notebooks support both languages in the same notebook.

Assume this data comes directly from the streaming platform and hasn't been cleaned, so review it carefully before relying on the numbers. When something looks off, work out what it is and decide how to handle it; when a definition or a final decision is genuinely ambiguous, choose a reasonable option and explain why. There's often more than one valid way to compute a result, so explain the approach you take and the reasoning behind your choices.

What's assessed: your results, and how clearly you explain what you see in the data and justify the choices you make.