Staff Data Scientist

Responsibilities

Company Measurement and Causal Strategy

• Define and own Fetch's company-level measurement framework anchored in MAU × ARPU.
• 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, marketing mix modeling, 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 pricing and incentive tests.
• 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 organization.
• 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.

Requirements

Minimum Qualifications

• 8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company- or organization-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 in ads ranking, relevance, retrieval, recommendation, or personalization systems.
• Experience in ad measurement, attribution, incrementality, experimentation, or related areas.