Note: The job is a remote job and is open to candidates in USA. Tonal is a company focused on innovative fitness solutions, and they are seeking a Senior Data Scientist to drive causal and machine learning-based analyses. The role involves collaborating with product and analytics teams to build causal inference tools, define KPIs, and deliver actionable insights that influence product strategy and business outcomes.
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
Skills
- 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
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