Senior Data Scientist I, Monetization
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Summary
Hands-on senior data scientist role owning advertising audience and measurement products built on Fetch's receipt-scanning purchase data: defining shopper audiences, extending them beyond Fetch's user base, and measuring whether ad campaigns changed real purchases. Core work is SQL/Python statistics, propensity modeling, causal inference, and product collaboration.
Meet Fetch AI & Data
AI & Data at Fetch sit at the center of how we understand our business, make decisions, and build intelligent products. The organization operates as an integrated AI & data ecosystem, spanning multiple disciplines, including data engineering, analytics engineering, machine learning, experimentation, and data platforms, all working together to turn data into durable business and customer impact.
Teams operate in complex problem spaces where requirements evolve, tradeoffs are constant, and the right answer is rarely obvious. Success depends on strong technical judgment, comfort with ambiguity, and the ability to gather context and make informed decisions while balancing quality, performance, scalability, and responsible use.
Practitioners across this org contribute hands-on to production systems, analytical foundations, and intelligent features. You will collaborate closely with product, platform, and engineering partners, help shape standards and best practices, and ensure our AI and data capabilities scale reliably as Fetch grows.
About the Role
Fetch is built on real purchase data, shared by millions of users scanning their receipts. In Monetization, we turn that signal into products advertisers rely on: audiences built from actual shopping behavior, and measurement that shows whether a campaign changed what people bought.
As a Senior Data Scientist I, you’ll independently own a defined audience or measurement product, from framing the problem to delivering it. Most of your work will support our third-party data products business, and you’ll also apply what you learn to first-party user monetization. You’ll own the full loop: building informed audiences, extending them beyond Fetch’s user base, measuring off-platform outcomes through receipts, and using those results to make the next audience better.
This is a hands-on scientific and product role. You’ll partner closely with Product, Engineering, Sales, and GTM, and you’ll be in the room with customers explaining what the product does, why it works, and where its limits are.
This is a full-time role that can be held from one of our US offices or remotely in the United States.
What you’ll do at Fetch
- Independently own a defined audience or measurement product, from problem framing through delivery
- 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 while keeping observed purchases distinct from inferred propensity
- Combine receipt, partner, panel, survey, and contextual data into reliable datasets, accounting for linkage uncertainty, missingness, and coverage, and applying weighting or calibration where warranted
- Design credible studies linking ad exposure to receipt-observed purchases, using experiments when feasible and well-justified observational methods when they aren’t
- Check receipt coverage, exposure matching, selection bias, and uncertainty, and keep attribution and incrementality distinct
- Turn customer problems into scoped products with clear success criteria and launch recommendations, balancing audience quality, reach, advertiser value, user value, revenue, and cost
- Feed campaign outcomes back into audiences and models, using holdouts and fresh data to confirm improvements are real
- Partner with Sales and GTM to shape pilots and explain the product’s value, presenting methods and limitations without overpromising precision or lift
- Build reproducible datasets, analyses, and validation checks, partnering with Product, Engineering, and ML engineers to move prototypes into production
- Document your methods, share reusable code, and pass on what you learn to the team
In your Toolbox
- 5+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
- Experience owning an audience, measurement, or data product from an ambiguous question through delivery and a business decision
- A track record of working regularly with product, engineering, and commercial partners, with the ability to clearly explain both the customer need and your own contribution
- Experience building useful datasets from fragmented, incomplete, or biased sources, and showing how you assessed their validity
- Strong population inference skills, including generalizing from an observed sample to a broader population and checking who’s missing
- Experience building propensity or audience models and validating them against real-world usefulness (population coverage, calibration, and measured outcomes), not just offline accuracy, then using the results to improve the next iteration
- Hands-on causal inference experience, including designing an experiment or observational study you can defend (the comparison, assumptions, diagnostics, and limits) and separating attribution from true incrementality
- Strong SQL, Python, applied statistics, and predictive modeling skills, plus a habit of building reusable, reproducible assets
- The ability to explain scientific findings clearly to nontechnical and customer audiences
- Sound judgment about when to move independently and when to seek review because assumptions or scope have outrun the evidence
Nice to haves
- Applied experience in monetization data science, audience development, advertising measurement, market research, or consumer panels, especially where you tied data work to customer or commercial value is strongly preferred
- Experience working with panel, survey, consumer research, or purchase data
- A background in political science, survey methodology, econometrics, demography, or another quantitative field where you built strong datasets with limited resources and made explicit inferences about populations from sparse data
