Jobs

Data Science Manager - Credit Risk Modelling


Job details
  • Harnham
  • London
  • 2 months ago
Applications closed

Data Science Manager – Credit Risk Modelling

£90-120,000 (dependent on experience)

London

Great opportunity with a leading UK FinTech at the forefront of the market. This business offer a fast-paced and driven work environment, with a really collaborative team culture that encourages employee development and growth.

THE COMPANY

This successful FinTech is a leader in the market and are going from strength to strength. They are a dynamic and fast-paced lender and are seeking a driven and experienced individual to join their team in building out their predictive models using cutting-edge Machine Learning techniques. This role is an opportunity for someone to be part of a successful company that is continuing to grow whilst driving impact in your work at the forefront of the market.

THE ROLE

  • Work across a range of credit models within the business, predominantly scorecards and broader decisioning models
  • Using innovative machine learning techniques to further enhance the model suite and drive profitability across the business
  • Own the deployment and implementation of predictive models across the product suite
  • Working closely with the Credit and Product teams to enhance performance and profitability across the business by collaborating on strategies and model enhancements

YOUR SKILLS AND EXPERIENCE:

  • Essential to have experience developing predictive models within a Credit Risk setting
  • SQL and Python experience is essential
  • Must have end-to-end experience in the model lifecycle, from scoping, development, enhancement
  • Experience in a fast-paced environment and ability to work across multiple projects, in a FinTech

SALARY AND BENEFITS

  • Base salary from £90-120,000 depending on experience
  • Share options
  • Company pension scheme
  • Private medical care

HOW TO APPLY

Please register your interest by sending your CV to Rosie Walsh through the ‘Apply’ link

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