Lead Credit Risk Data Scientist - End-to-End ML & Deployment

Broadgate; West Midlands; On-site; GBP 70,000 - 105,000; Full time

Job summary

Broadgate is seeking a data-driven professional to lead the development and implementation of credit decisioning models, owning the full model lifecycle from problem definition through deployment and monitoring. You will collaborate across Risk, Product and Technology to embed models into lending processes.

You will build ML and statistical models for credit risk, affordability and pricing, develop monitoring dashboards, explore new data sources and experimentation techniques, and translate

Qualifications

• Strong experience developing machine learning models within lending
• Must have - Python and SQL
• Proven experience owning the full model development lifecycle
• Experience in an automated credit decisioning environment
• Good understanding of UK credit risk principles and regulatory requirements
• Ability to communicate technical concepts clearly to technical and commercial audiences

Responsibilities

• Develop and deploy machine learning and statistical models covering credit risk, affordability and pricing
• Own the end-to-end modelling lifecycle, from data preparation through to deployment and monitoring
• Build monitoring frameworks and dashboards to assess model performance
• Explore new data sources, challenger models and experimentation techniques
• Translate analytical outputs into clear, actionable insights
• Work with Risk, Product and Technology teams to embed models into lending processes

Skills

Python

SQL

Machine learning

Job description

Broadgate is seeking a data-driven professional to lead the development and implementation of credit decisioning models, owning the full model lifecycle from problem definition through deployment and monitoring. You will collaborate across Risk, Product and Technology to embed models into lending processes.

You will build ML and statistical models for credit risk, affordability and pricing, develop monitoring dashboards, explore new data sources and experimentation techniques, and translate