Data Scientist, Cashflow Forecasting

Company Description

PatientFi® is a technology-based, point-of-sale financing company in Irvine, CA that partners with healthcare providers to offer patients a friendly payment solution for out-of-pocket medical and dental procedures. The company serves various healthcare specialties, including plastic surgery, dermatology, ophthalmology, dentistry, fertility, and medical spas. PatientFi's mission is to expand patient access to elective healthcare treatments by removing the cost barrier and providing patients with a convenient payment option.

Job Description / Responsibilities

As a Data Scientist, Cashflow Forecasting at PatientFi, you will be a key member of the Data Science team, focused on building and maintaining the models and processes that project portfolio cash flows. You will develop and validate forecasting models, deliver monthly forecasts, and document your work, while serving as a trusted partner to Finance, Capital Markets, and other internal teams. You will also help answer questions from internal stakeholders and from external and prospective lending partners.

Key responsibilities include:

• Build, enhance, and maintain cashflow forecasting models for the loan portfolio, including the underlying component models that drive projected performance
• Develop machine learning and statistical models to support business use cases and data-driven decision-making across the organization
• Perform regular back-validation and ongoing performance monitoring of forecasting models, comparing forecasts to actuals, diagnosing variances, and recommending refinements
• Produce monthly cashflow forecasts, including reviewing assumptions, reconciling results, and clearly communicating the key drivers of change
• Create and maintain thorough model documentation covering methodology, assumptions, data lineage, limitations, and validation results
• Partner with Finance, Capital Markets, and other internal teams to support their forecasting needs, including reporting, planning, funding and investor analyses, and ad-hoc scenario and sensitivity analysis
• Respond to questions and information requests from internal teams as well as external and prospective lending partners, clearly explaining forecasting methodology, assumptions, and portfolio performance
• Use Python and SQL to build reproducible, well-structured forecasting pipelines and automate recurring forecasting and reporting processes
• Apply AI and LLM tools to accelerate analysis, coding, documentation, and reporting while maintaining accuracy, rigor, and appropriate controls
• Translate technical results into clear, actionable insights for both technical and non-technical audiences
• Collaborate with fellow data scientists and teammates, and contribute to team knowledge sharing and best practices

Desired Skills / Experience

Required qualifications:

• 2+ years of experience at a financial services company (such as lending, fintech, banking, asset management, or capital markets)
• Master’s degree or higher in Finance, Statistics, Mathematics, Economics, Engineering, Computer Science, or a related quantitative field
• Proficiency in Python (e.g., pandas, NumPy, statsmodels, scikit-learn) and SQL, with a strong understanding of coding best practices and model documentation
• Hands-on experience using AI and LLM tools to improve analytics, coding, and productivity
• Excellent written and verbal communication skills, with the ability to explain complex modeling concepts clearly to finance teams, business stakeholders, and external partners
• Strong interpersonal skills and a collaborative style; able to build trusted working relationships across teams and engage confidently with external partners
• Strong analytical rigor and attention to detail, with the ability to manage multiple deadlines within a recurring monthly forecasting cycle
• Self-motivated, results-oriented, and comfortable working in a fast-paced environment

Preferred qualifications:

• Experience in financial modeling and cashflow forecasting
• Knowledge of forecasting model components such as PD, EAD, LGD, and SMM
• Experience with machine learning and statistical modeling techniques (e.g., gradient boosting, regression, survival analysis, time series) and the end-to-end model development lifecycle
• Experience with consumer lending or healthcare financing, including loan-level performance data, delinquency, and roll rates
• Experience with model validation, back-testing, and model documentation standards
• Experience with Google Cloud (BigQuery) and BI tools such as Looker, Tableau, or Power BI