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# Data Scientist

- **Company:** Data Science
- **Location:** Slough, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Data Analysis, Cluster Analysis, Continuous Integration, Information Engineering, Python (Programming Language), NumPy, Azure Machine Learning, Software Deployment, Pandas, Scikit Learn
- **Published:** September 28, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/data-scientist/48869712

## About the Role

business answerWhat you'll bring2-3+ years' commercial Data Science experienceSolid statistics: regression, classification, clustering, time series, experimental designStrong Python: pandas, NumPy, scikit-learnModels taken beyond the notebook and into productionMLOps, CI/CD and deployment knowledge, or close work with engineersA practical approach: explainable over unnecessarily complexEnjoys collaborating and explaining ideas to technical and non-technical colleaguesNice to haveFinancial services, fintech or another regulated environmentMicrosoft Fabric, Azure ML or DatabricksMLflow, DVC, GitWhy this teamYou'll join an established, supportive data team with a modern platform ready to go, colleagues who value Data Science, and real scope to shape how modelling is used across the business.Interested? Message me or apply below.

## Description

Data ScientistBerkshire · Hybrid · Salary depending on experience + benefitsFinancial ServicesWant to join a data team that's ready for Data Science?The foundations are built. The platform is live, the engineers and analysts are in place, and the team is now adding a Data Scientist to turn that into models that shape real decisions across customer behaviour, risk and operational efficiency.You'll work day to day with Data Engineering and Analytics colleagues who already understand what Data Science needs to succeed.What you'll doTake business problems from first question to deployed modelBuild and validate models in Python, then monitor them in productionUse statistics, experimentation and A/B testing to test ideas properlyPartner with Data Engineering on pipelines, features and productionisationSupport Analytics colleagues on more complex statistical problemsShare knowledge and help set best practice across the teamExplain results clearly to senior stakeholders, focused on the


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