Senior Data Scientist

About This RoleAI processing…

Key Responsibilities

• 1
  Design, implement, and productionalize statistically rigorous causal analyses to quantify the impact of product features on user behaviors, engagement metrics, and downstream business outcomes
• 2
  Develop and maintain causal frameworks that link product interventions to behavioral change, engagement shifts, and business performance
• 3
  Select and apply appropriate experimental and observational methods, leveraging regression- and ML-based approaches where appropriate to control for confounding and heterogeneity
• 4
  Validate causal findings through robustness checks, sensitivity analyses, and clear articulation of assumptions and limitations
• 5
  Translate results into clear, actionable recommendations that inform product strategy, marketing decisions, and executive-level prioritization
• 6
  Develop analytical notebooks and workflows that are reproducible, scalable, and suitable for deployment in production environments
• 7
  Partner with product and cross-functional stakeholders to define feature-level engagement and efficacy KPIs aligned with business objectives
• 8
  Incorporate model-derived signals (e.g., predicted engagement, risk scores, uplift estimates) into KPI frameworks where appropriate to improve measurement and decision-making
• 9
  Implement testing, documentation, and versioning practices to ensure KPI definitions are reliable, discoverable, and consistently interpreted
• 10
  Maintain metric documentation and metadata to support self-service analytics and cross-functional consumption
• 11
  Design and deliver high-quality visualizations in Looker and Databricks that clearly communicate analytical and ML-driven insights without requiring supplemental explanation
• 12
  Ensure visual outputs are intuitive, decision-oriented, and aligned with established data visualization best practices
• 13
  Incorporate generative AI capabilities into visualization and analytics assets where appropriate to improve interpretability and cross-functional adoption
• 14
  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

Requirements

• 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
• Languages: sql, python, r

Perks & BenefitsTypical for this role