PatientFi

Compensation

Full Description

Job Description / Responsibilities As a Data Scientist at PatientFi, you will play a key role in developing industry-leading machine learning models for managing credit and fraud risks. You will work with multiple complex data sources, such as credit bureau reports and customer-supplied information, to optimize underwriting decisions, approve/decline strategies, credit line assignments, and fraud detection methodologies. Key responsibilities include: Develop and implement machine learning models for credit risk assessment and fraud detection, ensuring compliance with lending best practices and regulatory requirements Build and improve quantitative and qualitative models (including CECL, Prepayment, Weighted Average Remaining Maturity (WARM), Probability of Default and Loss Given Default (PD/LGD) methodologies) Leverage advanced data analytics to dynamically segment applicants and loans based on behavior and performance Optimize risk-based pricing strategies, underwriting criteria, and collections strategies using data-driven insights Collaborate with engineers to deploy machine learning models into production environments Monitor, analyze, and report on model performance, ensuring continual refinement and adaptation to changing market conditions Develop LookML and SQL queries to build dashboards in Looker for tracking model and business performance Extract the most value from data to drive key business metrics and enhance risk management strategies Conduct ad-hoc analysis to support risk management, investor services, operations, and corporate development Support analysis and reporting in stress testing models Desired Skills / Experience 1+ years of experience in Data Science, Credit Risk, Fraud Risk, Quantitative Analytics, or related fields Advanced degree (M.S./PhD preferred) in Statistics, Computer Science, Engineering, Economics, or a related quantitative field 1+ years of relevant experience within consumer credit risk management, ideally at a FinTech startup, banking or lending company; bonus points for healthcare experience Expertise in Python and SQL, with a strong understanding of coding best practices and model documentation Experience implementing data pipelines using Google Cloud products (BigQuery, GCS, Cloud DataFlow, Cloud Pub/Sub, Cloud BigTable) Understanding of data warehousing concepts, data engineering, and data modeling Strong experience in risk modeling, fraud detection, and machine learning techniques applied to financial services. Strong communication and interpersonal skills, with the ability to clearly translate technical insights to business stakeholders Self-motivated, results-oriented, and capable of managing multiple projects in a fast-paced environment Experience working with Looker (or similar BI tools like Tableau, Power BI) to design reports/dashboards Familiarity with bureau data and alternative data sources for credit and fraud risk analysis Knowledge of cash flow modeling and loss forecasting is a plus