Key Responsibilities

• Developing, testing, deploying, and maintaining machine learning and statistical models in production environments.
• Building scalable data science solutions using Python and SQL.
• Applying software engineering best practices, including version control (Git), code reviews, testing, and documentation.
• Using MLOps practices to support model deployment, monitoring, governance, and continuous improvement.
• Working with large and complex datasets.
• Evaluating emerging technologies and analytical techniques to identify opportunities for innovation and business value.
• Ensuring models and analytical solutions meet regulatory, governance, and risk management standards within a financial services environment.
• Contribute to backlog refinement and sprint planning, stand-ups and retrospectives.
• Reinforce Agile ways of working, using pair programming, DORA insights and best practices to help to foster an environment of continuous improvement within the team.

What We’re Looking For

Research (by Harvard University) shows that women are particularly likely to second guess themselves and not apply - so if you are worried you don't meet all the criteria, get in touch anyhow and let us do the worrying…

• Strong practical knowledge of statistics, mathematics, and machine learning techniques, with experience developing, validating, deploying, and monitoring predictive and analytical models in production environments.
• Advanced Python and SQL skills, with experience using tools such as Jupyter/JupyterHub to conduct data exploration, develop machine learning solutions, and support production workflows.
• Hands-on experience applying machine learning techniques using frameworks such as XGBoost, PyTorch, or similar technologies to solve complex business problems and deliver measurable value.
• Experience translating business requirements into actionable analytical solutions, working closely with stakeholders to identify opportunities, define approaches, and deliver data-driven outcomes.
• Strong communication skills, with the ability to explain complex technical concepts and analytical findings to both technical and non-technical audiences.
• Experience working with modern data platforms, software engineering best practices, and data science tooling to develop scalable and maintainable analytical solutions.
• Experience with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), or other emerging AI technologies is advantageous.

Why join Handelsbanken

We want everyone at Handelsbanken to feel supported, motivated, and able to do great work. That’s why, alongside brilliant colleagues, meaningful work and training and development opportunities, we provide a variety of benefits designed with your wellbeing in mind.

In addition, there is a flexible benefits package with the key highlights detailed below.

Application next steps

Your journey with us begins once you have submitted your application. One of our Handelsbanken Talent Acquisition Partners will be reviewing your details and will later organise a phone conversation if your experience aligns with our requirements, we will extend an invitation for you to participate in an interview.

This advert will be live for a minimum of two weeks. However, please note that after the two weeks, the closing date could change at any time depending on the number of responses received.

The Bank is deeply committed to embedding good equality and diversity practice into all of our activities. This is so that we are an inclusive, welcoming and inspiring place to work that encourages everyone to apply, regardless of socio-economic background, age, disability, pregnancy and/or parental status, race (including colour, nationality, and ethnic or national origin), veteran status, marital and civil partnership status, religion or belief, sex, gender reassignment or sexual orientation.