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Quantitative Risk Analyst

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Onsite

Quantitative Risk Analyst

SEB

Quantitative Risk Analyst

Skills

PythonData VisualizationComplianceRegulatory Compliance

1 month ago

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Important information


Contract type:

Permanent

Location:

Solna, Sweden

Work mode:

Onsite

Published on:

7 September 2026

What they need


Do you have passion for quantitative analysis, credit risk modelling and turning complex data into meaningful insights? Join SEB’s Credit Risk team as a Quantitative Credit Risk Analyst and help develop, monitor and improve models that support key business and regulatory decisions. You will work with advanced analytics in a dedicated expert team with broad exposure across the bank, while growing in a supportive culture that values learning, innovation and career development.

We are looking for A Quantitative Credit Risk Analyst to join our Credit Risk team in SEB. You will be responsible for development and review of quantitative models that assess credit risk. The role will require to use mathematical, statistical, and machine learning techniques to analyse large datasets, including historical loan performance, customer data, and macroeconomic indicators, to extract trends and construct risk factors. You will be part of a dedicated, expert team with broad exposure across the bank and excellent prospects for personal and career growth.

About The Role As a Quantitative Credit Risk Analyst, you will:

  • Development and review of credit risk models aligned with banking regulations and business needs
  • Perform modelling using readymade-tools, focus on modelling rather than coding
  • Collaborating with business areas to provide insights into data and explain model outputs
  • Monitoring and back-testing credit risk models to ensure accuracy and regulatory compliance
  • Contributing to various projects related with data quality, regulatory etc. within credit risk

To thrive in this role, we believe you have: To succeed in this role, we believe that you:

  • Have strong background in mathematics, statistics, econometrics, finance, or related quantitative fields
  • Are experienced with programming languages such as Python, SAS
  • Have a genuine curiosity for data and ability to work with large datasets and use statistical software and data visualization tools
  • Have good communication skills, with the ability to convey complex technical concepts and insights to diverse stakeholder groups

Nice to have:

  • Experience in model development, review within credit risk contexts
  • Familiarity with credit risk frameworks, including PD/LGD/CCF modelling and IRB approaches
  • Educational background in statistics, mathematics or econometrics

What we offer:

  • Friendly and welcoming culture
  • Challenging and interesting projects at the forefront of credit risk modelling
  • A supportive environment for learning, development and career progression
  • Attractive compensation and benefits

Ready to take the next step on your career journey?

Welcome to our inclusive culture, where our shared values inspire and uplift our team. We celebrate diversity and strive to ensure every employee feels seen, heard, and valued.

Since we select candidates continuously, feel free to send in your application today, but no later than 30 of September 2026. If you have questions about the position, please contact Indra Wilson at [email protected].

Please be aware that our final candidates undergo background checks, a process that includes for example identity control, verification of qualifications, credit checks, company engagements and criminal records checks. In some cases, we also apply random drug checks.

SEB Sweden has a redeployment responsibility, why this position might be covered by internal redeployment.

Learn more about working at SEB www.sebgroup.com/career

It is our fundamental belief that inclusion and diversity is crucial for our future success. We strive to have an inclusive, value-driven culture where employees feel valued, respected and involved irrespective of who they are, what they believe or where they come from.

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