Data Scientist

The role sits at the intersection of statistical rigor and product impact: designing experiments, building predictive models, and translating messy production data into decisions that shape the product roadmap.

The role sits at the intersection of statistical rigor and product impact: designing experiments, building predictive models, and translating messy production data into decisions that shape the product roadmap.

You will partner closely with ML engineers and product teams to move models from notebook to production - including causal inference work, A/B testing infrastructure, and forecasting systems used across the business.

• Design and analyze A/B tests and quasi-experiments, defining metrics, guarding against common pitfalls (novelty effects, network effects, Simpson's paradox)
• Build and validate predictive models (gradient boosting, time-series forecasting, causal models) using Python and libraries such as scikit-learn, XGBoost, and stats models
• Develop and maintain data pipelines in SQL and PySpark/ dbt, ensuring reproducibility and data quality for downstream modeling and reporting
• Partner with ML engineers to product ionize models, defining offline evaluation criteria, monitoring thresholds, and drift detection
• Conduct exploratory analyses on large-scale event, transactional, and behavioral data to surface actionable insights for product and growth teams
• Communicate findings to technical and non-technical stakeholders through clear write-ups, dashboards (Tableau/Looker), and stakeholder presentations
• Contribute to experimentation standards and documentation, raising the analytical bar across the data science team

• 3–6 years of experience in data science, applied statistics, or quantitative research, with experience shipping analyses and models that drove real decisions
• Expert-level SQL and strong Python; comfort working with large datasets in Spark or similar distributed frameworks
• Hands-on experience designing and analyzing A/B tests, including power analysis, variance reduction techniques (e.g., CUPED), and sequential testing
• Solid grounding in statistics and causal inference: regression, difference-in-differences, propensity score methods, uplift modeling
• Experience with at least one BI/visualization tool (Tableau, Looker, Mode) and workflow tooling (dbt, Airflow)
• MS or PhD in a quantitative field (Statistics, CS, Economics, Operations Research) or equivalent industry experience
• Bonus: experience with LLM-based tooling for analysis or synthesis, Bayesian experimentation platforms, or prior work at a high-growth product company