The increasing volume, complexity, and regulatory scrutiny of financial‑crime and compliance risks requires advanced, data‑driven capabilities that traditional analytics and manual processes can no longer support. The Compliance Data Scientist will significantly enhance Nium’s ability to detect emerging risks, optimise controls, meet regulatory expectations, and drive operational efficiency across the compliance function. This role fills a critical capability gap and directly supports strategic priorities including automation, risk‑based decisioning, model optimisation, data‑quality improvement, and regulatory assurance. Additionally, this role is specifically designed to support activities related to transitioning compliance systems to advanced, data-driven Artificial Intelligence / Machine Learning solutions e.g. Transaction Monitoring detection models. Role Summary: Responsible for development of AI/ML models to identify risks (such as fraud or credit risk) while ensuring these models are auditable, explainable, and compliant with data privacy laws
Key Responsibilities
Design, deploy, and monitor predictive models and AI algorithms to detect anomalies, fraud, or potential breaches
Conduct deep-dive analyses into risk events, identifying root causes to improve risk strategies and operational workflows
Analyze large datasets to identify patterns, anomalies, and emerging risks.
Performs data validation, cleansing, and reconciliation for regulatory reporting.
Ensure alignment with regulatory requirements by building scalable reporting platforms and documenting data protocols
Partner with legal, product, and operations teams to translate complex technical findings into actionable business insights for senior management
- Maintains auditability, traceability, and evidence generation within systems.
- Requirements
- Degree in Statistics, Mathematics, Data Science, Economics, or related quantitative field
4-6 years in data science, advanced analytics, or machine‑learning roles.
Proficiency in various model development techniques
Prior experience in financial services, fintech, payments, or consulting would be strongly preferred
Exposure to financial‑crime systems (e.g., transaction monitoring, sanctions screening, case‑management platforms).
Experience supporting compliance operations, investigations, or model governance.
Experience building and deploying ML models in production environments.
Strong analytical rigor, proactive problem-solving, and capability to communicate technical concepts to non-technical partners
Self-motivated, adept in working individually and as part of a global team
