Data Modeller

• Significant professional experience in model risk management, model validation and model governance.
• Strong knowledge of data science, statistics and quantitative analysis.
• Proven ability to produce high-quality technical and analytical documentation, including model documentation.
• A higher degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Actuarial Science, or a comparable field.
• Excellent analytical and problem-solving capabilities.
• Strong communication, presentation and stakeholder management skills.
• A structured and organised approach, combined with the ability to work independently and take ownership.
• Confidence in communicating recommendations, establishing direction and delivering outcomes in a fast-changing environment.

We are looking for a seasoned Lead Developer to take ownership of the development, validation and governance of transaction monitoring models within a Financial Crime Prevention / AML environment.

You will work with a major Nordic banking organisation, combining strong expertise in model risk and quantitative analysis with a practical, hands-on approach to model development. The role also involves shaping governance standards and supporting the continued development of the Transaction Monitoring Unit (TMU).

Hybrid work model.

,[Lead the design, development and validation of transaction monitoring models, ensuring robust documentation and governance throughout the model lifecycle., Take responsibility for model governance activities within the TMU, including validation reviews and incorporating feedback from second-line risk functions., Define approaches to model development and experimental design, balancing methodological rigor with business and delivery requirements., Work closely with analytics teams to document model methodology, results, assumptions, limitations and potential weaknesses., Support the development and testing of analytical approaches and prototypes., Coach and collaborate with team members on establishing and improving model-related procedures and standards, including data lineage, data governance, calibration, tuning and model performance monitoring., Contribute to the continuous improvement of model operating procedures and analytical practices., Build effective relationships with internal and external stakeholders and represent the organisation in a professional manner.] Requirements: SQL, AML, risk management, Statistics Additionally: Sport subscription, Private healthcare.