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Salary: USD 159,945 - 188,171 / annual
Fetch is a rewards platform that has awarded over $1 billion in Fetch Points to users by turning everyday activities—grocery shopping, dining, mobile gaming—into meaningful rewards. The company processes over 13 million receipts daily, providing visibility into $212 billion in gross merchandise value, and operates as an outcomes-based advertising platform helping brands acquire and retain consumers.
In the Monetization team, you will independently own a defined audience or measurement product from problem framing through delivery. Your work will primarily support third-party data products for advertisers, with secondary applications to first-party user monetization. You'll own the complete loop: building informed audiences, extending them beyond Fetch's user base, measuring off-platform outcomes through receipt data, and iterating based on results.
Key responsibilities include:
- Independently own audience or measurement products end-to-end
- Translate advertiser goals and purchase behavior into audience definitions, labels, and predictive models
- Build and validate statistical expansion beyond Fetch users, testing quality, calibration, and reach on held-out populations
- Combine receipt, partner, panel, survey, and contextual data into reliable datasets, accounting for linkage uncertainty and coverage
- Design credible studies linking ad exposure to receipt-observed purchases using experiments or well-justified observational methods
- Assess receipt coverage, exposure matching, selection bias, and uncertainty; keep attribution and incrementality distinct
- Turn customer problems into scoped products with clear success criteria and launch recommendations
- Feed campaign outcomes back into audiences and models using holdouts and fresh data to confirm improvements
- Partner with Sales and GTM to shape pilots and explain product value, presenting methods and limitations transparently
- Build reproducible datasets, analyses, and validation checks with Product, Engineering, and ML engineers
- Document methods, share reusable code, and mentor the team
This is a hands-on scientific and product role requiring close collaboration with Product, Engineering, Sales, and GTM teams. You'll engage directly with customers explaining product capabilities, why it works, and its limitations.
REQUIREMENTS:
- 5+ years of experience in data science, machine learning, or applied analytics with demonstrated ownership of complex analytical problems in product-driven environments
- Proven track record owning an audience, measurement, or data product from ambiguous question through delivery and business decision
- Regular collaboration with product, engineering, and commercial partners with ability to clearly explain customer needs and your contribution
- Experience building useful datasets from fragmented, incomplete, or biased sources and assessing their validity
- Strong population inference skills: generalizing from observed samples to broader populations and identifying missing populations
- Experience building and validating propensity or audience models against real-world usefulness (population coverage, calibration, measured outcomes), not just offline accuracy
- Hands-on causal inference experience: designing experiments or observational studies you can defend (comparison, assumptions, diagnostics, limits) and separating attribution from true incrementality
- Strong SQL, Python, applied statistics, and predictive modeling skills with habit of building reusable, reproducible assets
- Ability to explain scientific findings clearly to nontechnical and customer audiences
- Sound judgment about when to work independently versus seeking review
NICE TO HAVES:
- Applied experience in monetization data science, audience development, advertising measurement, market research, or consumer panels, especially tied to customer or commercial value
- Experience with panel, survey, consumer research, or purchase data
- Background in political science, survey methodology, econometrics, demography, or quantitative field where you built strong datasets with limited resources and made explicit population inferences
