Senior Data Scientist II, Monetization

Fetch•US - Remote,

5h•Hybrid

About The Position

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 II, you’ll own the scientific and product roadmap for a Monetization domain, connecting first-party user monetization with our third-party data products. You’ll lead the full flywheel: informed audience creation, statistical expansion beyond Fetch’s user base, receipt-based off-platform measurement, and continuous audience improvement. This is a hands-on product leadership role. You’ll act as owner and seller for your domain alongside Product, Engineering, Sales, and GTM, shaping what gets built, how it’s positioned, and what claims the evidence can support. This is a full-time role that can be held from one of our US offices or remotely in the United States.

Requirements

8+ years of experience in data science, machine learning, or applied analytics, with demonstrated leadership of complex analytical products in product-driven environments
Experience defining a scientific or data-product roadmap, and the ability to explain what you prioritized, what you declined, and why
Experience leading cross-functional delivery with Sales, GTM, Product, and Engineering, including shaping customer pilots and defending product value directly
A track record of building differentiated datasets or audience products from imperfect sources, with clear trade-offs on coverage, validity, and cost
Experience with audience expansion, panel calibration, data fusion, or measurement products that held up beyond the original sample
Ownership of complex causal measurement, including defending your assumptions under scrutiny and changing a recommendation when the evidence fell short
Judgment on reach–precision and revenue–cost trade-offs, including recognizing when more scale reduced value
Sustained audience or monetization gains and customer adoption, with measured outcomes used to redirect product strategy
Evidence of influencing senior stakeholders, such as times when evidence changed a pitch, commitment, or investment
Hands-on depth in SQL, Python, statistical modeling, and causal inference
Experience mentoring scientists and establishing reusable methods that improved quality beyond your own work

Nice To Haves

Substantial ownership of audience, measurement, or monetization data products in advertising, retail media, market research, consumer panels, or a comparable commercial setting is strongly preferred
Experience translating market research or quantitative social science into repeatable products and customer or commercial decisions
A background in political science, survey methodology, econometrics, demography, or another resource-constrained quantitative field

Responsibilities

Own the scientific and product roadmap for your domain, deciding what to build, what to sequence, and what to decline
Set the standards for how audiences are created, evaluated, and expanded beyond Fetch users
Make the call on reach–precision and revenue–cost trade-offs, favoring scalable products over one-off customization
Shape how we combine receipt, partner, panel, and survey data, and decide which data investments are worth making
Own measurement methodology for complex studies linking ad exposure to receipt-observed purchases
Define what results can and can’t support, especially when a causal claim or generalization goes beyond the evidence
Turn bespoke studies into repeatable products
Build the feedback loop between audiences and measurement, with the holdouts and monitoring to trust what it tells you
Connect first-party monetization with third-party products, including using audience insights to acquire and engage high-value users
Lead the scientific case with Sales and GTM, from customer discovery and pilots to positioning and handling buyer objections
Influence senior stakeholders and commercial commitments with clear, evidence-backed recommendations
Stay hands-on in SQL, Python, statistical modeling, and causal inference
Mentor scientists and establish methods that raise quality across the domain