Senior Data Scientist

Tonal is a company focused on leveraging unique experiences to enhance team strength. They are seeking a Senior Data Scientist to drive causal and machine learning-based analyses that measure the impact of product features on user behavior and business outcomes, collaborating closely with product and analytics teams.

Responsibilities:

• 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
• 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
• 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