Vacant job

Do you want to work at the intersection of advanced statistics, programming, and business operations? Do you have a genuine interest in predictive modeling and machine learning, and do you want to turn complex data into concrete strategic decisions? The Credit Function at PayEx is now looking for a Data Scientist who wants to help develop and modernize their analytical models. We have more data, better tools, and more knowledge of industry standards than ever before – now we are looking for you to help us build the next generation of models!

About the role

The Credit Function at PayEx is responsible for overarching, quality-assured credit risk management. You will join their analytics team, which currently consists of analysts and Data Engineers. Here, you will become a key player in developing their models, with a specific focus on IFRS 9 modeling (PD, LGD, EAD). You will work closely with their Senior Data Scientist, giving you fantastic opportunities for professional development and experience sharing, while quickly taking on significant independent responsibility.

The role involves much more than just training models behind a screen. You will dive deep into their databases, talk to product owners, prepare materials, and present concrete proposals for changes to the organization.

What we offer

• PayEx offers an stimulating environment where you get to work with modern algorithms and large datasets to create real value in a central part of our banking operations.
• PayEx offers a culture with plenty of room for own initiatives and good development opportunities.
• A fixed-term employment with Academic Work with a fixed monthly salary. Good opportunities for permanent hire exist for the right person!

Responsibilities

The role involves working with the entire chain from data analysis and modeling to presenting business-oriented proposals for product owners and decision-makers.

• Model Development & IFRS 9: Work with the design, development, and implementation of modern ML and predictive models for credit risk. You contribute to building new and maintaining existing models for Expected Credit Loss (ECL) in accordance with IFRS 9 requirements.
• In-depth Portfolio & Behavior Analysis: Conduct financial data analysis to identify risks and opportunities. You analyze payment behaviors per customer segment (e.g., private individuals) to understand why a specific segment is not paying, and what drives their behavior.
• Business Insight & Strategy: Translate quantitative results into clear insights. You prepare materials that support decisions on credit templates, process improvements, and collection strategies, and present these to relevant stakeholders.
• Data & Regulatory Compliance: Work closely with our data team to ensure high data quality and efficient pipelines. You also contribute to model validation and documentation in line with requirements from the Swedish Financial Supervisory Authority and external auditors.

We are looking for you who

• Hold a Master's degree or equivalent in a quantitative field, such as mathematics, statistics, computer science, financial economics, technical physics, industrial economics, systems science, or similar.
• Have advanced knowledge of Python or R.
• Have good knowledge of SQL.
• Have experience with AI/ML modeling and predictive analysis.
• Have practical skills in building and training models.
• Have fluent skills in Swedish and English.

It is an advantage if you have

• Knowledge of the IFRS 9 regulation.
• Experience with cloud-based data and analysis tools.
• Previous experience from regulated financial operations.

To succeed in the role, you have the following personal qualities:

• Goal-oriented.
• Social.
• Organized.
• Responsible.
• Intellectually curious.

Our recruitment process

This recruitment process is handled by Academic Work, and our client's request is that all questions regarding the position are sent to Academic Work.

We apply continuous selection and will remove the ad when enough candidates have reached the final stage of the recruitment process. A CV is required when applying. We do not use cover letters as a selection method, so one is not needed. The recruitment process includes two selection tests: a personality test and a cognitive ability test. The tests are a tool to find the candidate with the highest potential for the position and to promote equality, diversity, and a fair recruitment process.