Data Scientist

Data Scientist

There is a big difference between building a model that works in a notebook and building one that changes how a business makes decisions.

This role is focused on the second.

You'll join the Data Science team of a large, established financial services business. The company has invested in its data platform and is building a dedicated ML Engineering capability to get more models into production.

You'll work on real business problems, using large and varied datasets to develop models that can be deployed, monitored and used across the organisation.

What you'll work on

You'll take ownership of data science projects from the initial problem through to production. That will include:

Working with stakeholders to turn business problems into clear data science use cases. Exploring and preparing complex datasets. Building, testing and evaluating machine learning models. Using techniques such as regression, classification, clustering and forecasting. Writing clean, reusable Python code that can move beyond the research stage. Working with ML Engineers to deploy models into production. Defining how model performance should be measured and monitored. Reviewing existing models and identifying where they should be improved, retrained or replaced. Explaining your findings to both technical and non-technical audiences. Contributing to better standards across experimentation, documentation and model governance.

You'll have the freedom to explore different approaches, but this is not a research-only position. The aim is to build models that solve genuine problems and continue performing once they are live.

What we're looking for

You'll have commercial experience as a Data Scientist and a track record of taking machine learning projects beyond initial experimentation.

You should be comfortable with:

Python and common data science libraries. SQL and working with large datasets. Statistical modelling and machine learning. Feature engineering, model selection and evaluation. Writing clear, maintainable and testable code. Git-based development workflows. Working in a cloud environment such as Azure, GCP or AWS. Communicating complex findings in straightforward language.

Experience within financial services or insurance would be useful, but it is not essential. The team is more interested in your ability to understand a problem, choose the right approach and build something people can actually use.

Why consider it?

You won't be joining a team that produces interesting models only for them to sit unused in a notebook.