Data Scientist
PatientFi
full-time
Posted on:
Location Type: Remote
Location: California • United States
Visit company websiteExplore more
Job Level
About the role
- 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
Requirements
- 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.
Benefits
- Health insurance
- Professional development opportunities
- Remote work options
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningcredit risk assessmentfraud detectionquantitative modelingCECLProbability of DefaultLoss Given DefaultPythonSQLdata pipelines
Soft skills
communicationinterpersonal skillsself-motivatedresults-orientedproject managementcollaborationanalytical thinkingadaptabilityproblem-solvingstakeholder engagement
Certifications
M.S.PhD