Credit Risk Analyst β€” Data Science & Modelling (Hybrid) in Swindon

Credit Risk Analyst β€” Data Science & Modelling (Hybrid) in Swindon

Swindon Full-Time 63000 - 77000 Β£ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Support credit risk models using regression and machine learning techniques.
  • Company: Join Nationwide's innovative Risk Decision & Data Science team in Swindon.
  • Benefits: Flexible hybrid work setup with opportunities for professional growth.
  • Other info: Collaborative environment with strong support from experienced Risk Managers.
  • Why this job: Make a real impact on decision-making processes while developing your data science skills.
  • Qualifications: Experience in data analysis and coding, preferably with SAS.

The predicted salary is between 63000 - 77000 Β£ per year.

Nationwide is seeking a Risk Analyst to join the Risk Decision & Data Science team in Swindon. You will support credit risk decision models across application, behavioural and collections portfolios, employing regression and modern machine learning techniques to improve decision tools and outcomes for members.

Under a Risk Manager's guidance, you will build relationships across the business, write SAS code, and present insights to drive better decisions while working in a flexible hybrid setup.

Credit Risk Analyst β€” Data Science & Modelling (Hybrid) in Swindon employer: Description This

As a Customer Banking Advisor at our Cleckheaton branch, you will be part of a dynamic team that prioritises exceptional customer service and innovative banking solutions. We offer competitive benefits, including annual bonuses and comprehensive training programmes, fostering an environment where employees can thrive and grow in their careers. Join us to experience a supportive work culture that values adaptability and personal development.

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Contact Details:

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We think you need these skills to ace Credit Risk Analyst β€” Data Science & Modelling (Hybrid) in Swindon

Credit Risk Analysis
Data Science
Modelling Techniques
Regression Analysis
Machine Learning
SAS Programming
Data Presentation