Senior Analyst, Cash Flow Underwriting & Intelligence

One Park Financial•Plano, TX

About The Position

One Park Financial (OPF) is a financial technology company that connects small businesses with flexible financing and funding options. The Senior Analyst will own bank-transaction data as an analytical asset and use it to guide underwriting, portfolio monitoring, pricing, renewals, and growth decisions. The role analyzes deposits, balances, withdrawals, NSF activity, and revenue trends across applicants and funded merchants to identify business health, emerging portfolio risks, and merchants who may be good candidates for approval or faster offers. This is an action-oriented analytical role rather than a reporting role. The analyst will work with Pricing, Credit, Data Engineering, and operations teams to validate signals and translate them into decisions and production criteria. The work includes identifying early indicators of loss, finding strengthening segments and approval opportunities, evaluating applicants and merchants who do not receive funding as a market signal, and measuring the effects of business actions. The analyst will also build lightweight tools and automation, including AI- and LLM-assisted workflows, and communicate findings to non-technical and executive audiences. OPF describes its culture as collaborative, innovative, inclusive, and supportive of professional growth. The company emphasizes continuous learning, training, career advancement, and the use of modern technology.

Requirements

5–8 years of experience in data or credit analytics; experience in financial services, lending, or fintech is preferred.
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 working with large datasets and performing analysis in pandas or notebooks.
Fluency with bank-transaction and cash-flow data, including understanding deposits, NSF activity, balance trends, partial account coverage, seasonality, and survivorship bias in a funded book.
Hands-on experience with Plaid and connected-account transaction feeds, including their structure, quirks, and coverage gaps.
Credit-risk judgment that can distinguish segment weakness from the need to tighten criteria, and can identify when data supports approving more businesses.
Understanding that correlation alone is not a pricing decision.
Experience building portfolio-monitoring or performance-tracking metrics, cohort or vintage analyses, and operational dashboards.
Statistical judgment, including validating signals out of sample and accounting for regime shifts and base rates.
Strong business judgment, communication skills, and ability to distill complex analysis for 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

Own analysis of merchant financial health using bank-transaction data, including cash runway, deposit velocity, revenue volatility, NSF activity, and negative-day patterns across applicants and funded merchants.
Build and maintain portfolio health metrics, cohort views, and early-warning dashboards to monitor performance and identify emerging portfolio stress.
Identify transaction trends that lead delinquency or losses and turn validated indicators into portfolio monitoring.
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 OPF declined and businesses that declined OPF—as market signals about industry trends.
Identify industries and segments that may be mispriced relative to their cash-flow performance.
Work with Pricing and Credit to translate analysis into actions, including faster approvals and improved offers in strengthening segments and tighter criteria in weakening segments; 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 narratives.