Senior Analyst, Portfolio Strategy

One Park Financial•Salt Lake City, UT

16h

About The Position

One Park Financial (OPF) is a leading Financial Technology company dedicated to empowering small businesses by connecting them with a wide variety of flexible financing and funding options. Our mission is to provide entrepreneurs with the working capital they need to elevate their businesses to new heights. At OPF, we believe in working with high-performing individuals who are ready to play an integral part in our company's expansion. We know that our success hinges on our people, and we strive to enable them to do what they do best. At OPF, we foster a dynamic and inclusive company culture that emphasizes collaboration, innovation, and personal growth. Our team is composed of passionate, driven individuals who are committed to making a difference. This is the pricing and portfolio strategy seat, and it is one of the highest-leverage analytical roles at the company. You will design and size pricing and credit strategies and then prove out what they actually delivered for the business and our customers. You will work across the whole chain, because it is one system: the funnel that brings small businesses to us, the models that score them, the way we build their offers, and the portfolio that tells us whether it all worked. What sets the bar here is a focus on real, realized outcomes rather than surface metrics. You will be expected to build the economic case behind a recommendation, quantify what it is worth, and be honest about what you do not yet know. You will partner with Data Science, with the engineering team behind our offer systems, and directly with Credit, Pricing, and Sales leadership on the decisions themselves. We are hiring two of these roles deliberately. This function is central to how the business runs, and it should not sit one-deep. We are a very AI-forward company and expect our analysts to work that way, leaning on modern AI and LLM tooling to make their analysis faster and sharper. We want high-performing people who see the whole picture and make everyone around them better.

Requirements

5 to 8 years in credit analytics, pricing analytics, portfolio strategy, or risk strategy, preferably in Lending, Fintech, Specialty Finance, or Financial Services.
Bachelor's or master's degree in a quantitative field (economics, statistics, mathematics, finance, computer science, or similar).
Strong SQL and strong Python with pandas.
You pull large, messy, multi-source data yourself and do the real analysis in Python, comfortable building a model of the economics rather than only summarizing history.
Genuine unit-economics fluency on a loan or advance book: you can build the cash-flow math from first principles rather than only reporting yield off someone else's work.
Portfolio analytics depth: vintage and cohort analysis, roll rates, loss curves and forecasting, and maturity-adjusted views. You know why comparing a young vintage to a mature one without adjustment is wrong.
Statistical honesty. You separate description from causation, check base rates and sample sizes, and look for the outlier carrying a dollar-weighted metric before you trust the cell.
You have influenced a real decision: taken an analysis to people who own a P&L, been pushed on it, and had a policy or price change as a result.
Excellent business judgment and the ability to distill complex analysis for executive audiences.

Nice To Haves

Experience with merchant cash advance, revenue-based finance, small-business lending, or another short-duration product, and with credit scorecards, decision engines, or pricing systems (a strong plus).
Experience with dbt, cloud data environments (AWS), and modern AI tooling (LLMs, agents) on your own data (a plus).

Responsibilities

Design and size pricing and credit strategies, building the case before it ships with clear options and a sensitivity band, so leadership is choosing between real alternatives rather than approving a single number.
Ground every recommendation in projected, realized economics rather than surface metrics or what a deal promises on paper.
Monitor portfolio performance continuously: vintage and cohort trends, roll rates, loss and delinquency trends, and renewal performance. Know which way the book is moving before someone asks.
Measure what strategy changes actually did once they go live, designing the test or comparison well enough that the answer means something.
Find and size automation and growth opportunities, and partner with Pricing and Engineering to put them into production.
Watch portfolio composition, not just the metrics: mix shifts over time can move a number without anything real changing, and catching that is part of the job.
Partner with Data Science on model development and validation, and build your analysis so it can be rerun: documented, version-controlled work rather than a spreadsheet only you can open.
Present to executives, including the CEO, with a clear recommendation, a defensible number, and an honest account of what you do not know.