Manager of Credit Risk Analytics #LI-Remote
Pasadena, California /
Risk – Credit Risk /
Scratch Financial ("Scratchpay") is a financial technology startup based in Los Angeles, California. Our goal is to make difficult financial decisions simple and increase accessibility to fair, affordable, and transparent medical financing. Driven by our award-winning technology, Scratchpay has become the fastest growing financing provider in veterinary care, with our payment plans now offered in over 10,000 practices across the U.S. and Canada–ranging from dental offices to optometry clinics. With long-established industry giants in our sights, we’re looking to shake up the patient payments space, and we’d love to have you come aboard for the ride!
If putting compassion first, helping create groundbreaking products and continuously iterating & refining those products sound like you, then we encourage you to apply.
Scratchpay’s Credit Risk team utilizes analytical tools to develop strategies governing automated credit decisions that yield optimized portfolio performance. Reporting directly to the Chief Credit Officer (CRO), the Credit Risk Manager will utilize their well-honed analytical abilities to develop strategies using advanced models and internal/external data to improve credit underwriting decisions and overall portfolio performance. The best candidate will have strong, practiced analytical skills that they can apply from multiple perspectives as they pursue the best solution to a challenge. A strong candidate will also be able to lead the evaluation of alternative data and its integration into Scratchpay’s data infrastructure.
Minimum Requirements of the Role
- Minimum of Bachelor's degree in business, statistics, mathematics, or other relevant quantitative field, or similar level of knowledge acquired through on-the-job experience
- Experience managing credit risk and/or fraud and/or data science in consumer lending industry
- Strong background and practical experience in statistical or econometric modeling, model validation
- Strong communication skills – ability to clearly and succinctly communicate technical subject matter to other team members and Senior Management team
- Knowledge and experience with data segmentation, statistical procedures, decision tree, and modeling skills
- Advanced knowledge SQL, R/ Python required
- Demonstrated experience with machine learning models
- Strong interpersonal skills – collaborate with people across functions and develop strong relationship
- Ability to work in fast-paced environment and meet deadlines without sacrificing quality
- We will also consider candidates who don’t have financial services experience but have strong technology background and analytical skills
Duties of the Role
- Develop credit strategies to enhance credit underwriting performance by using data, machine learning, and advanced analytics tools to optimize risk decisions
- Design and execute comparative testing scenarios in order to evaluate different risk strategies
- Develop innovative risk solutions utilizing new data, analytics, and models
- Evaluate alternative data and new solutions to optimize credit strategy and improve credit performance
- Build and validate models used in support of Credit Risk goals
- Be the primary liaison to other departments for maintenance and development of risk data infrastructure and partnerships with third party data providers
- Conduct ad-hoc analyses related to risk management, investor services, product, and regulatory requirements
- Analyze portfolio performance at a granular segment level on an ongoing basis. Identify trends and conduct root-cause analysis to isolate key performance drivers. Communicate findings and recommendations
- Work with legal and compliance team to ensure strategies and policies comply with all the regulatory requirements
- Supervisory responsibility for training and development of other Credit Risk analytics associates
Scratchpay is committed to diversity in its workforce and is proud to be an equal opportunity employer. Scratchpay considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.