Data Scientist
Metrics and Measurement
You'll define, instrument, and maintain the Discovery metrics framework across web, mobile, and TV. Model metrics such as precision, recall, coverage, and diversity, as well as customer metrics like click-through rate (CTR), playthrough, completion, session depth, and cold-start ramp time. You'll also focus on business metrics like retention segmented by recommendation engagement.
Experimentation
Own the A/B testing and experimentation pipeline for Discovery surfaces. Design experiments with statistical rigor, including sample sizing, duration, segmentation, and guard-rail metrics. Build the institutional muscle so the team can ship with evidence, not opinions. We use GrowthBook.
User Behavior Analysis
Decode how members discover, browse, and engage with content across three very different platforms. Identify patterns in Guild voting, theatrical-to-streaming conversion, content affinity, and churn risk. Surface the insights that change how the product team thinks about the problem.
Causal Inference
Distinguish correlation from causation in engagement data, where selection bias is everywhere. When recommendation engagement correlates with retention, determine whether the system is driving retention or whether high-intent users are simply more likely to click. Design quasi-experiments when randomization isn't feasible.
Data Foundations for Analytics
Build and maintain the dbt models, data pipelines, and analytical infrastructure that make data accessible and trustworthy for the Discovery team and the broader organization. Ensure the data is right, because if it's wrong, nothing else matters.
Trajectory from Data Scientist to ML Engineer
The trajectory from a Data Scientist to an ML Engineer is explicit and supported on this team. As the analytical foundation matures, the work shifts from feature engineering for recommendations to evaluating new signals and building them. You'll prototype recommendation approaches and evaluate them against the golden eval set you built in your first months. You'll then graduate to owning a model from experimentation to deployment, writing testable Python, managing data lifecycles, and thinking about systems design.