Senior Data Scientist I, Monetization

OpenKrill; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Fetch is seeking a Senior Data Scientist I to own defined audiences and measurement products, from framing problems to delivering results. You’ll work with product, engineering, sales, and GTM to translate goals into reliable audience definitions and predictive models.

You’ll build reproducible datasets, validate approaches, and communicate limitations clearly to customers. This is a full-time role, eligible for US offices or remote work within the United States.

Qualifications

• 5+ years of data science, ML, or analytics in product-focused settings.
• Experience owning an audience, measurement, or data product from question to delivery.
• Strong collaboration with product, engineering, and commercial teams.
• Ability to create datasets from fragmented or biased sources and assess validity.

Responsibilities

• Independently own a defined audience or measurement product from framing to delivery.
• Translate advertiser goals and purchase behavior into audiences and models.
• Build and validate expansion beyond Fetch users with careful calibration.
• Combine multiple data sources into reliable datasets and apply weighting where needed.
• Design studies linking ad exposure to receipts using experiments or observational methods.

Skills

Data science

Causal inference

Audience modeling

Experiment design

Cross-functional collaboration

Communication to customers

Tools

SQL

Python

Applied statistics

Predictive modeling

Job description

What we’re building and why we’re building it.

Fetch helps people live rewarded every day, with a vision to become the rewards destination for everyone. We turn everyday activities into meaningful rewards, whether it’s grocery shopping, grabbing a quick meal, or playing a favorite mobile game. To date, we’ve awarded more than $1 billion in Fetch Points to our users.

Each day, more than 13 million receipts are submitted on Fetch, providing visibility into over $212 billion in gross merchandise value. This creates the largest retail-agnostic, SKU-level view of household spending, powering Fetch as an outcomes-based advertising platform that helps brands acquire and retain lifelong consumers.

The Fetch app is available on the App Store and Google Play, with more than 6 million five-star reviews from a highly engaged and loyal user base.

It’s not just our users who believe in Fetch: with investments from Softbank, ICONIQ, DST, Greycroft, and partnerships ranging from challenger brands to Fortune 500 companies, Fetch is reshaping how brands and consumers connect in the marketplace. When you work at Fetch, you play a vital role in a platform that drives brand loyalty and creates lifelong consumers with the power of Fetch points. User and partner success are at the heart of everything we do, and we extend that same commitment to our employees.

At Fetch, we value curiosity, adaptability, and the confidence to explore new tools, especially AI, to drive smarter, faster work. You don’t need to be an expert, but you should be ready to learn quickly and think critically. We welcome learners who move fast, challenge the status quo, and shape what’s next, with us. Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, Fetch fosters a people-first culture rooted in trust, accountability, and innovation. We encourage our employees to challenge ideas, think bigger, and always bring the fun to Fetch.

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

Compensation:At Fetch, we offer competitive compensation packages including base, equity, and benefits to the e