Principal Data Scientist

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:

Upstart's Product & Growth Analytics team drives strategic insights and evidence-based decision-making across our expanding portfolio of credit products. Our team works at the intersection of product strategy, data science, and business impact, partnering with product leaders, the ML organization, and finance to shape how Upstart evolves its offerings and delivers value to borrowers and banking partners.

We're entering a new chapter. As Upstart expands into subscription-based products, we need to rethink how we approach data science and analytics from the ground up. This requires people who don't just answer questions, but ask the right ones first. People who dive into ambiguity, surface what matters, and guide the business toward smarter decisions.

As a Principal Data Scientist, you'll be a strategic thought partner to product, marketing, finance, and ML leaders, helping them optimize the decisions in their domains. You'll combine deep technical expertise with the judgment to know which problems merit rigorous experimentation, which require causal inference, and which first demand foundational business understanding. You will additionally build out technical tools to improve our understanding of the business, with examples including forecasting models, spend allocation optimization, marketing incrementality, and attribution models.

How you’ll make an impact

Shape strategic direction by identifying the gaps and questions others miss. You're not waiting for orders; you're setting the agenda.

Be a thought partner to leaders across product, finance, and ML. You help them understand complex problems from first principles and define what actually matters.

Design, build, and own tools that are leveraged across the team, such as models for forecasting change, optimizing spend allocation, marketing incrementality, and attribution.

Design and execute experiments, from classic A/B tests to complex causal inference analyses, with full ownership of hypothesis development, experiment design, and results interpretation.

Build metrics frameworks and measurement strategies, helping stakeholders understand what to optimize for and why.

Collaborate with our ML organization on high-complexity technical challenges, bringing statistical rigor and causal reasoning to our work.

Translate ambiguous, multifaceted business problems into clear frameworks that guide decision-making.

Take ownership of your work end-to-end: from scoping the question, to analysis, to storytelling, to driving organizational change based on insights.

Influence without direct authority across engineering, product, finance, and executive leadership, creating clarity and alignment where understanding is incomplete.

Minimum Qualifications

8+ years of experience in data science or quantitative roles with direct impact on product or business strategy.

Demonstrated mastery of experimental design and A/B testing, with rigorous experience designing and interpreting experiments and translating findings into meaningful business actions.

Deep statistical expertise, including causal inference methods, with the ability to select and apply the right method to ambiguous business problems and derive causal insights rather than surface-level correlations.

Strong coding experience in Python or equivalent, with a demonstrated ability to build technical tools that create leverage across the organization rather than just executing ad hoc analyses.