Data Science Manager - Credit Risk Modelling Immediate Start No Experience

Leading UK FinTech
Full Time 40 £90,000 - £120,000 Per year Greater London 41 Luke St, London EC2A 4DP, United Kingdom Apply before 2024-11-05
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Overview

Great opportunity with a leading UK FinTech at the forefront of the market. This business offers 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 is going from strength to strength. They are a dynamic and fast-paced lender seeking a driven and experienced individual to join their team in building out their predictive models using cutting-edge Machine Learning techniques. This role presents an opportunity to be part of a successful company that is continuing to grow while 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.
  • Collaborate closely with the Credit and Product teams to enhance performance and profitability by strategizing 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, to enhancement.
  • Experience in a fast-paced environment with the ability to work across multiple projects in a FinTech.

SALARY AND BENEFITS:

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

ABOUT THE COMPANY CULTURE:

The company fosters a collaborative environment that promotes growth and development, valuing each team member's contribution towards achieving collective goals.

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