Senior Data Scientist II, Monetization

Fetch; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Fetch is seeking a Senior Data Scientist II to own the scientific and product roadmap for Monetization, connecting first‑party monetization with third‑party data products. You will lead the flywheel from audience creation to measurement, balancing experiments, data quality, and business impact.

You will work directly with Product, Engineering, Sales, and GTM, shaping what gets built and how it’s positioned, while staying hands-on in SQL, Python, and causal inference to drive evidence-backed

Qualifications

• 8+ years of data science, ML, or applied analytics in product-driven environments.
• Experience defining a scientific or data-product roadmap and explaining prioritization.
• Experience leading cross-functional delivery with Sales, GTM, Product, and Engineering.

Responsibilities

• Own the scientific and product roadmap for your domain, deciding what to build and sequence.
• Set standards for how audiences are created, evaluated, and expanded beyond Fetch users.
• Lead measurement methodology for studies linking ad exposure to receipt-based purchases.
• Turn bespoke studies into repeatable, scalable products.
• Mentor scientists and establish reusable methods to raise domain quality.

Skills

Data science

ML / Applied analytics

SQL

Python

Causal inference

Leadership

Cross-functional

Product-driven

Audience data

Tools

SQL

Python

Job description

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 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 its 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.

What you'll do at Fetch

• 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

In your Toolbox

• 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