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# Senior Data Scientist

- **Company:** Castleisland
- **Location:** Greater London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Information Engineering, Fraud Prevention and Detection, Monitoring of Systems, Python (Programming Language), Machine Learning, Software Engineering, Supervised Learning, Data Analytics, Machine Learning Operations, Data Pipelines
- **Published:** September 9, 2026
- **Apply:** https://www.collegerecruiter.com/job/2858056257-senior-data-scientist

## About the Role

* 5+ years of experience across Data Science, Machine Learning, Software Engineering, or related disciplines.
 * Strong Python skills and experience working with large-scale datasets.
 * Proven experience developing and deploying machine learning models in production environments.
 * Strong understanding of supervised learning techniques, particularly classification problems.
 * Experience with anomaly detection, fraud prevention, risk modeling, or related domains.
 * Ability to translate business challenges into data-driven solutions.
 * Experience working cross-functionally with Product, Engineering, and business stakeholders.
 * Strong communication skills and a pragmatic approach to problem-solving.
 * Comfortable operating in fast-moving environments with high ownership., * Experience in financial crime, fraud prevention, AML, risk, or payments.
 * Experience in fintech, banking, or financial services.
 * Backend engineering experience and familiarity with production systems.
 * Experience building real-time decisioning or risk platforms.
 * Familiarity with modern MLOps practices and model monitoring.

## Description

* Design, build, and deploy machine learning models that solve real-world financial crime and risk challenges.
 * Work on problems such as fraud detection, chargeback prediction, anomaly detection, identity verification, and transaction monitoring.
 * Transform ambiguous business problems into measurable ML solutions.
 * Partner closely with Product and Operations teams to define requirements, success metrics, and decision frameworks.
 * Analyse large datasets to identify patterns, opportunities, and risks.
 * Build and maintain production-ready data pipelines and model-serving solutions.
 * Monitor model performance and continuously improve accuracy, reliability, and business impact.
 * Collaborate with Data Engineering and Backend Engineering teams to operationalise machine learning at scale.
 * Help shape the future of AI and machine learning capabilities across ARQ.


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