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.
Responsibilities
- 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.
- Applied Model Adaptation: Take existing models and architectures and adapt or fine-tune them to our data - applied, not from-scratch research.
- Production Ownership: Ship models to production and own them end-to-end: serving, monitoring, retraining, and iteration.
- Experimentation: Design and read experiments (A/B, causal) to prove real business lift, not offline metrics alone.
- Cross-Functional Partnership: Partner with Product, Content, and Growth to turn model outputs into decisions.
- Beyond Rule-Based Analytics: Replace hand-tuned heuristics with models that learn.
Requirements
- BSc in a quantitative field such as Data Science, Computer Science, Statistics, or Mathematics (must). MSc or PhD is a strong plus.
- 5+ years of working experience in Data Science or Machine Learning, shipping models to production.
- Direct, hands-on experience building recommendation and/or personalization systems in production (a firm requirement, not a nice-to-have).
- Strong applied ML / data science foundations: modeling, evaluation, feature engineering.
- Neural networks and deep learning (must): hands-on experience training and adapting deep learning models for real-world production use.
- Time series data (must): hands-on experience modeling temporal data, including forecasting, trends, seasonality, and noisy real-world signals.
- Ability to take a model or architecture and adapt it to real, messy data - practical, not purely theoretical.
- Production and engineering chops: get models serving reliably and keep iterating.
- Experimentation discipline: A/B testing, causal reasoning, honest metrics.
- Data fluency: SQL and large data warehouses, comfortable in the raw data.
- Clear communication and a bias to ship.
Huge Advantage - industry experience:
- Recsys at scale: retrieval / ranking, sequence or session models, embeddings.
- LTV, churn, or propensity modeling in a consumer / subscription product.
- Streaming, entertainment, consumer / mobile, or growth-marketing data.
- MLOps: feature stores, model serving, monitoring, retraining pipelines.
- Causal inference / uplift modeling.