Responsibilities
- Build the experimentation statistical engine — hypothesis testing, sequential analysis, variance reduction (CUPED), Winsorization, power analysis. Ensure statistical correctness across all experiment types.
- Design warehouse-native experimentation that runs analysis inside customer warehouses (Snowflake, Databricks, Redshift, BigQuery). Build modular, warehouse-agnostic abstractions for rapid new backend support.
- Lead adaptive experimentation — contextual bandit systems, Bayesian optimization, automated allocation beyond simple A/B tests.
- Drive the platform roadmap with product, design, and data science. Shape what we build, not just how.
- Collaborate cross-functionally with Warehouse Integrations, SDK, Platform, and Data Science teams.
- Mentor engineers and raise the team's bar for statistical rigor and system design.
- Own operational excellence — monitoring, observability, incident response, on-call. Robust telemetry and alerting.
