Principal Data Scientist, Marketing Measurement

About Upstart

At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.

As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.

We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you.

If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.

The Team:

Marketing Science sits within Upstart's Growth, Product, and Ops Analytics team and partners with growth marketing on every channel that brings borrowers to Upstart: direct mail, paid search and social, video, mobile, email, brand campaigns, and lending partnerships. Our job is to tell the business which of those dollars are working, and then help decide where the next one goes.

As our Marketing Measurement lead, you'll own how Upstart answers some key questions about its marketing spend. What did each channel actually cause? How should credit be shared across the touches a borrower saw before applying? Where should next quarter's budget go? The answers decide whether a dollar of spend buys a funded loan or takes credit for one that would have arrived anyway.

We're looking for a data scientist with deep experience in causal inference and marketing measurement to be the measurement partner for our marketing, finance, and product leaders, and the person they trust to say when the evidence is strong enough to move the budget.

How you’ll make an impact

Guide incrementality measurement across our marketing channels, working with the analysts who run tests in each channel on design and interpretation, so the team knows what each channel causes rather than what it gets credit for

Apply causal inference methods when a clean test isn't feasible, so budget decisions still rest on causal evidence

Build and maintain the attribution approach that splits credit across direct mail, digital, partnerships, and email, including channels where a borrower never clicks anything

Turn measurement results into spend allocation recommendations that marketing and finance use to plan budgets

Use marketing mix models alongside experiments and attribution, and work out which to trust when they disagree

Automate recurring measurement reporting, so your time goes to interpretation and recommendations

Mentor analysts across the team and review their measurement work

Minimum Qualifications

8+ years of experience in data science or a related quantitative role, much of it spent measuring marketing or growth spend

Hands-on experience designing and analyzing incrementality tests, and a solid grasp of causal inference beyond standard A/B testing

Working knowledge of marketing mix modeling and multi-touch attribution, including where each one breaks down

Strong Python and SQL skills

A track record of measurement work that changed how a marketing budget was spent

Clear communication with technical and non-technical audiences, and the judgment to push back when the evidence doesn't support the plan