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PatientFi · 1 day ago

Data Scientist

United States

Full-time

Remote

Entry Level

1+ years exp

PatientFi is a technology-based, point-of-sale financing company that partners with healthcare providers to offer patients a friendly payment solution for out-of-pocket medical and dental procedures. The Data Scientist will develop machine learning models for managing credit and fraud risks, optimize underwriting decisions, and collaborate with engineers to deploy these models into production environments.

FinanceFinancial ServicesFinTechHealth Care

No H1B

Hiring Manager

Annais G.

Responsibilities

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 mode

Qualification

Machine LearningPythonSQLRisk ModelingData PipelinesLookerData WarehousingCommunication SkillsInterpersonal SkillsSelf-motivated

Required

1+ years of experience in Data Science, Credit Risk, Fraud Risk, Quantitative Analytics, or related fields

Expertise in Python and SQL, with a strong understanding of coding best practices and model documentation

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

Familiarity with bureau data and alternative data sources for credit and fraud risk analysis

Preferred

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

Experience implementing data pipelines using Google Cloud products (BigQuery, GCS, Cloud DataFlow, Cloud Pub/Sub, Cloud BigTable)

Experience working with Looker (or similar BI tools like Tableau, Power BI) to design reports/dashboards

Knowledge of cash flow modeling and loss forecasting is a plus