We use PostHog and Clarity to improve the site, and Meta Pixel to measure page views and ad effectiveness. Usage data may include page addresses and browser identifiers. You can opt out of collection at any time. Details in the Privacy Policy
We are looking for an Applied Data Scientist to turn our data into models that decide what viewers watch next and how the app personalizes itself. Not rule-based dashboards, and not pure research - this is applied, practical ML in production: you take proven models and architectures, adapt them to our data, ship them into the product, and prove the lift.
You own recommendation and personalization systems end to end, from the raw behavioral signal to the model serving in the app, iterating on real outcomes: retention, completion, conversion, and LTV.
This is for data scientists who want their models in production changing what people watch, not sitting in a notebook. You're comfortable owning a problem from data to deployment, adapting the state of the art to messy real data, and measuring impact honestly.
What you'll do
Recommendation & Personalization: Own recommendation and personalization across the entire user journey: which series or episode to watch next, how series are ordered on screen, when and how notifications are sent, when a popup appears mid-viewing, which pricing offer a specific user sees - and more, as new surfaces come online.
Predictive Modeling: Turn raw behavioral data into predictive models for retention, churn, and conversion propensity. Predicted LTV (pLTV) is especially critical here - both to power personalization and as a signal we feed back to ad networks to help them find higher-quality users.