ML Professional - Retail Banking Lending - Credit Optimization

Our partners are one of the most established and larger Financial Technology Group of Companies of SEE.

They are currently expanding their ML credit portfolio solution services and are looking for an ML Professional in Credit Optimization.

We are on the search for an ML Professional in Credit Optimization, experienced in the use 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 the way the Bank assesses customers, manages credit exposure and balances growth, customer value and risk.

This is a hands-on ML / Data Science role applied to Credit — not a traditional Credit Risk position.

• →Develop, apply and continuously improve ML and predictive models for retail lending and credit decisioning.
• →Use customer, behavioral and transactional data to improve systems modeling in 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.

The role will contribute to:

• Better and more predictive credit decisions
• Improved balance between loan growth and credit risk
• More effective credit limits and customer exposure
• Earlier identification of credit deterioration
• Improved portfolio quality and risk-adjusted profitability
• A more data-driven and intelligent retail lending strategy

• Strong 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.
• Ability to work effectively with both technical and non-technical stakeholders.