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
• Deep statistical expertise, including causal inference methods.
• Strong coding skills.
• Track record of partnering with non-technical stakeholders to translate data into actionable business decisions, not just delivering analyses.
• Comfort operating in ambiguous environments where the right questions aren't predetermined, and the ability to shape direction through thoughtful inquiry.
• Exceptional communication skills, demonstrated ability to move fluidly between strategic questions and tactical details, and excellence in translating complex technical bodies of work into clear narratives.
• Exposure to ML applications and ability to partner effectively with ML/data science teams on technical challenges.
• Familiarity with personalization, recommendation systems, or matching algorithms.

Preferred Qualifications

• Experience with at least one of the following is highly preferred: forecasting, spend allocation optimization, marketing incrementality, or attribution.

Position location This role is available in the following locations: Remote 

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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