Staff Data Scientist

About the role

Fetch is at a critical inflection point in how data and science inform the company’s most important decisions. With millions of monthly active users, rich item-level purchase data, and increasing investment in AI-driven products like FetchGPT, Fetch has an opportunity to establish a rigorous, scalable measurement and causal reasoning foundation that powers pricing, incentives, growth, marketing investment, and financial planning. We are seeking a Staff Data Scientist to serve as the company-wide scientific and measurement leader.

Responsibilities

• Define and own Fetch’s company-level measurement framework anchored in MAU × ARPU. Company Measurement and Causal Strategy.
• Establish decision frameworks for pricing, incentives, and value trade-offs.
• Set standards for evidence quality, uncertainty, and confidence in decision-making.
• Define the causal reasoning model used across product, growth, marketing, and finance.
• Own the scientific capability roadmap including elasticity, value curves, MMM, and forecasting.
• Architect the semantic mart and metric logic powering FetchGPT and scalable insights.
• Define canonical metric definitions and unify logic across experimentation platforms, dashboards, and diagnostics.
• Partner with Analytics Engineering and Data Platform to build foundational data assets.
• Establish BI standards and eliminate redundant or conflicting dashboards.
• Serve as the quality bar for high-impact analytics and diagnostics.
• Review strategic analyses to ensure correct interpretation and mechanism alignment.
• Set scientific rules for experimentation and validate high-risk tests such as pricing and incentives.
• Ensure observational and experimental results reconcile cleanly.
• Create templates and interpretation guides to standardize rigor.
• Own cross-company models that drive executive decisions, including marketing mix modeling, elasticity and incentive sensitivity, value expectation curves, strategic forecasting, and financial mechanism models supporting MAU × ARPU planning.
• Organize and lead the scientific maturity of the data science and analytics organization.
• Author best-practice modeling libraries and documentation.
• Serve as a technical anchor and thought partner for senior ICs across the org.
• Establish norms for rigorous, transparent, mechanism-driven insights.
• Apply advanced statistical and causal methods to company-level problems.
• Build scalable, production-ready analytical frameworks in partnership with engineering.
• Champion best practices in experimentation design, model validation, and reproducibility.
• Leverage modern analytics tooling such as Python, SQL, Snowflake, dbt, and experimentation platforms.

Requirements

• 8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company and/or org-level problems.
• Deep expertise in causal inference, experimental design, and observational analysis, with demonstrated ownership of high-stakes business decisions informed by causal evidence.
• Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models used by senior leadership.
• Proven ability to influence and support executive decision-making, including presenting trade-offs, uncertainty, and recommendations that directly impact strategy.
• Exceptional written and verbal communication skills, with the ability to explain complex causal and modeling concepts to non-technical senior audiences.
• Bachelor’s degree in a quantitative field.

Preferred Qualifications

• Advanced degree in a quantitative discipline.
• Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts used in executive planning.
• Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems with complex value trade-offs.
• Experience defining semantic layers, metric governance, or data contracts at scale across multiple teams or functions.
• Demonstrated track record of org-wide scientific leadership without direct people management, including setting standards, reviewing work, and raising the technical bar across teams.