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OVERVIEW This Senior Data Scientist will drive causal and machine learning-based analyses to measure the impact of product features on user behavior, engagement, and business outcomes, translating results into clear, actionable recommendations. The role partners closely with product, analytics engineering, and fellow data scientists to build in-house causal inference tools, define KPIs, build production-ready analytical workflows, and deliver high-quality, governed visualizations. Success in this role requires strong statistical judgment, experience with product-driven ML, and a focus on delivering insights that are both trustworthy and immediately usable by cross-functional stakeholders KEY RESPONSIBILITIES Causal Inference - Design, implement, and productionalize statistically rigorous causal analyses to quantify the impact of product features on user behaviors, engagement metrics, and downstream business outcomes - Develop and maintain causal frameworks that link product interventions to behavioral change, engagement shifts, and business performance - Select and apply appropriate experimental and observational methods, leveraging regression- and ML-based approaches where appropriate to control for confounding and heterogeneity - Validate causal findings through robustness checks, sensitivity analyses, and clear articulation of assumptions and limitations - Translate results into clear, actionable recommendations that inform product strategy, marketing decisions, and executive-level prioritization - Develop analytical notebooks and workflows that are reproducible, scalable, and suitable for deployment in production environments KPI Development - Partner with product and cross-functional stakeholders to define feature-level engagement and efficacy KPIs aligned with business objectives - Incorporate model-derived signals (e.g., predicted engagement, risk scores, uplift estimates) into KPI frameworks where appropriate to improve measurement and decision-making - Implement testing, documentation, and versioning practices to ensure KPI definitions are reliable, discoverable, and consistently interpreted - Maintain metric documentation and metadata to support self-service analytics and cross-functional consumption Data Visualization - Design and deliver high-quality visualizations in Looker and Databricks that clearly communicate analytical and ML-driven insights without requiring supplemental explanation - Ensure visual outputs are intuitive, decision-oriented, and aligned with established data visualization best practices - Incorporate generative AI capabilities into visualization and analytics assets where appropriate to improve interpretability and cross-functional adoption - Support visualization governance by implementing CI/CD workflows, validation checks, and approval processes to ensure production dashboards meet quality and consistency standards prior to release QUALIFICATIONS - Strong statistical experience in causal inference methods like Difference in Difference, propensity score matching, regression discontinuity analysis, and randomized control trials to help link product changes to business outcomes - Applied experience building and evaluating machine learning models for prediction, segmentation, or uplift in a product or business context - Ability to develop reproducible, scalable analytical notebooks and workflows that transition effectively from development to production environments - Experience partnering with product teams to define feature-level KPIs and building robust, well-documented dbt models to expose those metrics across analytics layers - Strong track record of creating clear, decision-oriented visualizations in tools such as Looker and Databricks that communicate insights unambiguously to cross-functional stakeholders - 3-5+ years of experience - Tools and libraries: dbt, databricks, statsmodels, scipy, scikit-learn, causalml, prophet - Lan