ML Credit Optimization Lead – Retail Lending

humani; Kentucky; On-site; USD 90,000 - 135,000; Full time

Benefits offered by this job

Private medical insurance

Freelance/invoice work possible

Job summary

humani is seeking an ML Professional in Credit Optimization to apply Machine Learning, Data Science and advanced analytics to improve how the Bank makes retail lending decisions. The role sits at the intersection of Machine Learning, Credit and Business, using data and predictive models to optimise credit assessment and risk with a hands-on approach.

The successful candidate will develop and deploy models, leverage customer and transactional data, and work with Credit Risk, Data Science, Product

Qualifications

• Hands-on experience in Machine Learning, Data Science or Predictive Analytics.
• Experience applying ML to Credit, Lending, Risk, Financial Services or another highly data-driven decision environment.
• Strong understanding of modelling, data interpretation and model performance.
• Ability to connect technical analysis with real business and credit decisions.
• Strong analytical thinking, curiosity and a continuous optimization mindset.

Responsibilities

• Develop and improve ML models for retail lending and credit decisioning.
• Use customer, behavioral and transactional data to improve credit assessment, scoring and lending decisions.
• Optimize credit limits, approval strategies and risk-based decision rules.
• Identify patterns and signals that can improve the prediction of default, delinquency and customer behavior.
• Analyze portfolio performance and translate model outputs into opportunities for better risk-adjusted lending.
• Work closely with Credit Risk, Data Science, Product and Commercial teams to turn analytics into practical business decisions.
• Continuously test, challenge and refine existing credit strategies through data and experimentation.

Skills

Machine Learning

Data Science

Predictive Analytics

Credit Risk

Credit Lending

Job description

humani is seeking an ML Professional in Credit Optimization to apply Machine Learning, Data Science and advanced analytics to improve how the Bank makes retail lending decisions. The role sits at the intersection of Machine Learning, Credit and Business, using data and predictive models to optimise credit assessment and risk with a hands-on approach.

The successful candidate will develop and deploy models, leverage customer and transactional data, and work with Credit Risk, Data Science, Product