Senior Analyst, Cash Flow Underwriting & Intelligence

One Park Financial•Salt Lake City, UT

About The Position

One Park Financial (OPF) is seeking a Senior Analyst to own and leverage bank transaction data as an analytical asset. The role involves making critical decisions regarding loan approvals, portfolio health, and identifying growth opportunities. This position goes beyond traditional reporting, requiring the analyst to interpret data to assess segment health, guide pricing and renewal strategies, and utilize AI/LLM tooling to enhance analysis and integrate intelligence into business operations. The ideal candidate will be a high-performing individual who contributes to the overall success and expansion of the company.

Requirements

5-8 years of experience in data or credit analytics, preferably in Financial Services, Lending, or Fintech.
Bachelor's or Master's degree in a quantitative field (economics, statistics, mathematics, finance, computer science, or similar).
Strong SQL and Python skills; comfortable pulling large datasets and doing the real analysis in pandas/notebooks.
Genuine fluency with bank-transaction / cash-flow data. You know what a deposit, an NSF, and a balance trend actually tell you about a business, and where the data can mislead you (partial account coverage, seasonality, survivorship bias in a funded book).
Hands-on experience with Plaid. You've worked directly with connected-account transaction feeds and understand their quirks, coverage gaps, and how the data is structured.
A credit-risk instinct that runs both ways: you can separate "this segment is struggling" from "we should tighten," and you can spot where the data says we should be approving more, not less. You know a correlation is not a pricing decision.
Experience building portfolio-monitoring or performance-tracking metrics, cohort/vintage analysis, and dashboards that a business actually runs on (not one-off reports).
Statistical judgment. You validate a signal before you trust it (out-of-sample, regime shifts, base rates) rather than chasing the strongest correlation.
Excellent business judgment and communication, and the ability to distill complex analysis for executive audiences.

Nice To Haves

Experience with small-business, merchant cash advance, revenue-based finance, or fintech lending (a strong plus).
A real understanding of modern AI (LLMs, agents, MCPs) and comfort building lightweight tooling on your own data (a plus).
Experience with dbt (a plus).
Comfort with cloud data environments (AWS a plus).

Responsibilities

Own the read on merchant financial health from bank-transaction data (cash runway, deposit velocity, revenue volatility, NSF and negative-day patterns) across both applicants and funded merchants.
Own ongoing portfolio monitoring: build and maintain the health metrics, cohort views, and early-warning dashboards that tell us how the book is performing and where it's drifting, so the business sees stress building before it shows up in the numbers.
Find leading indicators of loss, segments where transaction trends move weeks ahead of delinquency, and turn them into monitoring that flags stress before it hits the portfolio.
Find where we should be growing: identify the merchants and segments whose cash-flow trends say we should approve them, and build the case to say yes faster, including candidates for auto-approval, pre-approved offers, and proactive renewals. You'll define the criteria and partner with Pricing to put them into production.
Use the applicant pool we don't fund as a market signal: businesses we declined and businesses that declined us are a less biased read on where industries are heading than our own book.
Surface pricing opportunities: industries and segments where we're mispriced relative to their cash-flow reality, in either direction.
Translate signals into action with the Pricing and Credit teams (faster approvals and better offers in strengthening segments, tighter criteria in weakening ones) and measure whether the actions actually worked.
Partner with Data Engineering on the pipelines that keep this data clean, current, and trustworthy.
Build lightweight tools and automation, including AI/LLM-assisted workflows, to scale the analysis and put self-serve intelligence in the hands of the operations teams.
Communicate findings clearly to non-technical and executive audiences with clean visuals and a clear story.