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# Senior Data Scientist - Risk

- **Company:** Harnham
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £60,000.0 - £90,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Information Engineering, Data Infrastructure, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Standard Sql, Transaction Data, Data Processing, Data Analytics, Machine Learning Operations
- **Published:** October 22, 2026
- **Apply:** https://www.reed.co.uk/jobs/senior-data-scientist-risk/57483524

## About the Role

* Strong commercial experience as a Data Scientist within a data-rich environment.
 * Advanced Python and SQL skills for analysis, modelling, and data manipulation.
 * Hands-on experience developing and deploying machine learning models.
 * Strong understanding of predictive analytics and statistical modelling techniques.
 * Experience translating complex analytical findings into clear business recommendations.
 * Ability to work effectively with cross-functional stakeholders across technical and non-technical teams.
 * Experience working with financial, lending, payments, or transactional data is advantageous.
 * Strong problem-solving skills and the ability to navigate ambiguity in a fast-paced environment.

## Description

As a Senior Data Scientist, you will play a key role in developing machine learning and predictive analytics solutions that support critical business and risk decisions., * Analysing large and complex datasets to identify trends, patterns, and actionable insights.

 * Building and deploying machine learning models to support forecasting, risk assessment, fraud detection, and customer outcomes.
 * Developing predictive models that drive commercial and operational decision-making.
 * Creating dashboards and visualisations to communicate findings to technical and non-technical stakeholders.
 * Partnering with product, engineering, and commercial teams to embed data-driven solutions into products and processes.
 * Working closely with data engineering teams to optimise data infrastructure and analytical workflows.
 * Supporting best practices across machine learning operations and model lifecycle management.