Senior Staff Data Scientist

8+ years of experience in data science, economics, statistics, or applied research Experience operating at Staff or Principal scope on company and/or org-level problems Deep expertise in causal inference, experimental design, and observational analysis Demonstrated ownership of high-stakes business decisions informed by causal evidence Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models Proven ability to influence and support executive decision-making Exceptional written and verbal communication skills Bachelor’s degree in a quantitative field Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems Experience defining semantic layers, metric governance, or data contracts at scale Demonstrated track record of org-wide scientific leadership without direct people management

Define and own Fetch’s company-level measurement framework Establish decision frameworks for pricing, incentives, and value trade-offs Set standards for evidence quality, uncertainty, and confidence Define the causal reasoning model used across departments Own the scientific capability roadmap Architect the semantic mart and metric logic powering FetchGPT Define canonical metric definitions and unify logic Partner with engineering to build foundational data assets Establish BI standards Serve as the quality bar for high-impact analytics Review strategic analyses Set scientific rules for experimentation Ensure observational and experimental results reconcile Create templates and interpretation guides Own cross-company models including marketing mix, elasticity, and forecasting Raise the scientific maturity of the data science organization Design upskilling programs Author best-practice modeling libraries Serve as a technical anchor and thought partner Establish norms for rigorous, transparent insights Apply advanced statistical and causal methods Build scalable, production-ready analytical frameworks Champion best practices in experimentation design, model validation, and reproducibility Leverage modern analytics tooling such as Python, SQL, Snowflake, dbt, and experimentation platforms