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. This role goes beyond traditional analytics or domain ownership. You will define how Fetch measures value, reasons about causality, and translates evidence into executive decisions. You will own core measurement frameworks, architect semantic and metric foundations, and set the scientific quality bar across analytics, experimentation, and strategic modeling.

Within your first year, you will define Fetch’s MAU × ARPU measurement operating system, establish canonical metrics and semantic standards powering FetchGPT and executive reporting, and deliver strategic models such as marketing mix, elasticity, and incentive sensitivity that directly inform leadership decisions.

What You’ll Do at Fetch:

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.

Semantic and Data Architecture

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.

Scientific Governance and Experimentation

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.

Strategic Modeling Ownership

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.

Org-Wide Scientific Leadership

Raise the scientific maturity of the data science and analytics organization. Design upskilling programs in statistics, causality, modeling, and storytelling. 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.

Technical Excellence

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.

Minimum Qualifications

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.

This is a full-time role that can be held from one of our US offices or remotely in the United States.

Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.