Senior Analyst, Cash Flow Underwriting & Intelligence

One Park Financial•Salt Lake City, UT

About The Position

One Park Financial (OPF) is a financial technology company that connects small businesses with flexible financing and funding. The Senior Analyst will own bank-transaction data as an analytical asset and use it to inform underwriting, portfolio health, pricing, approvals, renewals, and growth opportunities. The role examines cash-flow signals across applicants and funded merchants, including deposits, balances, withdrawals, non-sufficient funds (NSFs), and revenue trends. The position combines portfolio-risk monitoring with identifying businesses and segments that may merit faster approvals, auto-approval, pre-approved offers, or proactive renewals. The analyst will work with Pricing, Credit, Data Engineering, and operations teams to turn validated analytical findings into business actions, then measure their impact. This is an analytical and decision-support role rather than a reporting-only position. OPF describes itself as AI-forward and expects analysts to use modern AI and large language model tools to improve their analysis and help build intelligence into business operations. The role also involves creating lightweight tools and automation, maintaining trustworthy data pipelines in partnership with Data Engineering, and communicating findings clearly to non-technical and executive audiences.

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 such as economics, statistics, mathematics, finance, or computer science, or a similar field.
Strong SQL and Python skills, including experience analyzing large datasets using pandas and notebooks.
Genuine fluency with bank-transaction and cash-flow data, including understanding what deposits, NSFs, and balance trends indicate and how partial account coverage, seasonality, and survivorship bias can mislead.
Hands-on experience with Plaid and direct experience working with connected-account transaction feeds, their structure, quirks, and coverage gaps.
Credit-risk judgment that can distinguish between a struggling segment and a need to tighten criteria, and can identify cases where data supports approving more applicants.
Understanding that correlation alone does not establish a pricing decision.
Experience building portfolio-monitoring or performance-tracking metrics, cohort or vintage analyses, and business-used dashboards.
Statistical judgment, including validating signals out of sample and considering regime shifts and base rates rather than relying on the strongest correlation.
Strong business judgment and communication skills, including the ability to explain complex analysis to executive audiences.

Nice To Haves

Experience with small-business lending, merchant cash advance, revenue-based finance, or fintech lending.
Understanding of modern AI, including LLMs, agents, and MCPs, and comfort building lightweight tools using company data.
Experience with dbt.
Comfort working in cloud data environments; AWS experience is a plus.

Responsibilities

Analyze merchant financial health using bank-transaction data, including cash runway, deposit velocity, revenue volatility, NSF patterns, and negative-balance days, across applicants and funded merchants.
Own ongoing portfolio monitoring by building and maintaining health metrics, cohort views, and early-warning dashboards that show portfolio performance and emerging trends.
Identify leading indicators of loss and transaction patterns that signal potential delinquency or stress before it appears in portfolio results.
Identify merchants and segments whose cash-flow trends support approval, faster decisions, auto-approval, pre-approved offers, or proactive renewals.
Define approval and offer criteria and partner with Pricing to put them into production.
Use unfunded applicants, including businesses declined by OPF and businesses that declined OPF, as a signal of market and industry trends.
Identify industries and segments where pricing may not align with cash-flow conditions.
Translate analytical signals into actions with Pricing and Credit, including faster approvals and improved offers in strengthening segments or tighter criteria in weakening segments, and measure the results.
Partner with Data Engineering to maintain clean, current, and trustworthy data pipelines.
Build lightweight tools and automation, including AI- and LLM-assisted workflows, to scale analysis and provide self-service intelligence to operations teams.
Communicate findings to non-technical and executive audiences using clear visuals and concise narratives.