Staff Analyst, Investor Analytics

The Team: 

Upstart’s Investor Analytics team plays a pivotal role in securing capital, a critical function that powers our business. We deliver the analytical insight and technical capability behind how institutional capital partners understand and price Upstart’s credit performance, working across Capital Markets, Lending Partnerships, Machine Learning, Product, and Finance to keep that view accurate and current. Our priority right now is putting the credit groundwork in place for HELOC, Upstart’s home equity product as we go to market to raise capital,

As the Staff Analyst, Investor Analytics at Upstart, you will own the credit and performance story for our HELOC business end to end, building the performance data, automated monitoring, and recurring reporting that let us speak with authority about how the book is performing. You will partner closely with our Capital Markets, Lending Partnership, Machine Learning, and Product teams, work directly with some of the top institutional capital providers in the industry, and help model and steer deal strategy for our capital partners. We build with AI: the team uses agentic coding tools such as Claude Code and Codex daily to automate our credit pipelines, monitoring, and reporting, so more of your time goes to analysis and judgment and less to plumbing.

How you’ll make an impact

• Partner with Capital Markets and Lending Partnerships: Engage directly with capital investors to showcase the strength of Upstart’s platform, build lasting confidence, and craft tailored capital solutions for our home equity program.
• Own the credit performance narrative: Be the go-to voice on how the HELOC portfolio is performing — arrears and roll rates, vintage and cohort loss curves, model calibration and feature importance — and explain clearly to investors, partners, and internal leadership what is driving results and what it implies for forward risk.
• Deliver recurring performance reporting: Produce the yield, default, and prepayment views that our capital partners and internal teams rely on, on a reliable cadence, and use them to help model and steer deal strategy.
• Build credit intelligence infrastructure with AI: Develop the foundational financial models, data pipelines, and automated monitoring behind our credit reporting, using agentic AI tools such as Claude Code and Codex to build and maintain them so insights arrive faster and more reliably.
• Collaborate across teams: Work hand-in-hand with Machine Learning, Product, Engineering, and Finance on HELOC to shape credit metrics, craft compelling narratives, and develop financial forecasts, driving alignment on the state of the product.
• Mentor and elevate the team: Support junior team members while fostering a culture of excellence in data, credit, and analytics, positioning the team as a hub of expertise and innovation.

Minimum Qualifications 

• Extensive Industry Experience: 5+ years of professional experience in financial services, with a focus on credit and debt - consumer or structured credit, private credit, structured products, securitization, or credit capital markets - including hands-on work with loan portfolio performance and credit risk.
• Credit Analytics Expertise: Background in credit analytics with hands-on proficiency in Python, and SQL, and demonstrated experience analyzing and explaining portfolio credit performance, including delinquency and arrears, roll rates, vintage loss curves, and model calibration.
• Data-Driven Decision-Making: Expertise in working with large datasets, data modeling, and building robust data pipelines, utilizing tools like Databricks, Redshift, DBT, and business intelligence tools to deliver actionable insights.
• Quantitative Academic Foundation: Degree in Finance, Economics, Statistics, Engineering, Data Science, or another quantitative field, providing a solid grounding in the analytical and problem-solving skills this role requires.

Preferred Qualifications

• Proven Cross-Functional Collaboration: Exceptional track record of building partnerships with cross-functional teams, particularly in Finance, Product, Data Engineering, and Machine Learning, to align on strategy and drive impactful outcomes.
• Executive-Level Storytelling: Skilled at translating complex business and performance insights into compelling, executive-ready narratives, using data visualization tools like Tableau, PowerBI, or Looker to influence strategic decisions at the C-suite level.
• Home Equity or Mortgage Background: Familiarity with HELOC, home equity, or mortgage credit, including collateral valuation, lien position, CLTV, and property-level risk drivers. Consumer credit experience is the foundation for this role; home-secured experience is an added advantage.
• Financial Modeling and Deal Analytics: Experience building financial models, particularly lending cash flows, book-level profitability, or valuation, and using them to support pricing, structuring, or deal strategy decisions with capital partners. Familiarity with consumer credit capital markets, private credit, or structured products is helpful here.
• Comfort with AI-Assisted Development: Experience using agentic coding tools such as Claude Code, Codex, or similar to build and maintain analytics pipelines, automate recurring reporting, or accelerate exploratory analysis.
• Model Explainability and Calibration: Ability to interpret and communicate model performance, including calibration, feature importance, score-to-outcome alignment, and population drift, and to translate those findings for audiences without a modeling background.

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

Time zone requirements The team operates on the East/West coast time zones.

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 or Canada (outside of Quebec) but are expected to spend high quality time in-person collaborating via regular onsites and in-person meetings. The onsite 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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