Posted May 7, 2026
As a Data Scientist in this role, you will be responsible for the following key responsibilities:
Develop machine learning models and analytical solutions to identify suspicious patterns across AML, Fraud, Sanctions, KYC, Transaction Monitoring, and Behavioural Analytics domains. - Apply supervised and unsupervised ML techniques (clustering, anomaly detection, graph analytics) to uncover hidden networks and unusual behavior. - Build and evaluate risk-scoring models, entity resolution tools, and customer behavior segmentation frameworks. - Conduct exploratory analysis to identify typologies, red flags, and indicators of financial crime. You will also be involved in Data Engineering, Quality & Governance tasks such as:
Working with data engineering teams to build reliable data pipelines from internal and external financial crime datasets (transactions, customer profiles, network data, alerts, investigations). - Ensuring models, features, and datasets adhere to governance, model risk management (MRM), audit, and regulatory standards. - Maintaining rigorous documentation covering methodology, assumptions, validation, and performance monitoring. Your role will also include Business Engagement & SME Collaboration activities like:
Partnering with Financial Crime SMEs (AML Investigators, Fraud Analysts, Sanctions Screening teams) to translate typologies and regulatory requirements into data-driven models. - Providing explicit, actionable insights to Compliance, Operations, and Technology stakeholders. - Supporting the industrialization of analytics into production systems and operational workflows. In addition to the above responsibilities, you are required to have the following skills and qualifications:
Degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field. - Strong experience working with financial crime datasets or regulated financial services environments. - Proficiency in Python (Pandas, NumPy, Scikit-Learn, PySpark) and SQL. - Understanding of AML, Fraud, Sanctions, KYC, or Transaction Monitoring frameworks. - Familiarity with techniques relevant to financial crime detection: anomaly detection, clustering, graph/network analytics, NLP (for KYC docs, SAR narratives, etc.). - Experience with Azure, Databricks, or similar cloud environments. - Excellent communication skills, with the ability to simplify complex analytics for non-technical stakeholders. Preferred qualifications for this role include:
Experience in a bank or fintech Financial Crime / Compliance function. - Knowledge of regulatory requirements (FCA, PRA, MAS, FinCEN, FATF guidance). - Experience with machine learning model governance, explainability (XAI), and responsible AI practices. - Experience working with case management systems and screening tools (e.g., Actimize, Fenergo, NICE, Quantexa). - Exposure to graph databases (Neo4j, TigerGraph) or network analytics. - Experience implementing end-to-end MLOps workflows. As a Data Scientist in this role, you will be responsible for the following key responsibilities:
Develop machine learning models and analytical solutions to identify suspicious patterns across AML, Fraud, Sanctions, KYC, Transaction Monitoring, and Behavioural Analytics domains. - Apply supervised and unsupervised ML techniques (clustering, anomaly detection, graph analytics) to uncover hidden networks and unusual behavior. - Build and evaluate risk-scoring models, entity resolution tools, and customer behavior segmentation frameworks. - Conduct exploratory analysis to identify typologies, red flags, and indicators of financial crime. You will also be involved in Data Engineering, Quality & Governance tasks such as:
Working with data engineering teams to build reliable data pipelines from internal and external financial crime datasets (transactions, customer profiles, network data, alerts, investigations). - Ensuring models, features, and datasets adhere to governance, model risk management (MRM), audit, and regulatory standards. - Maintaining rigorous documentation covering methodology, assumptions, validation, and performance monitoring. Your role will also include Business Engagement & SME Collaboration activities like:
Partnering with Financial Crime SMEs (AML Investigators, Fraud Analysts, Sanctions Screening teams) to translate typologies and regulatory requirements into data-driven models. - Providing explicit, actionable insights to Compliance, Operations, and Technology stakeholders. - Supporting the industrialization of analytics into production systems and operational workflows. In addition to the above responsibilities, you are required to have the following skills and qualifications:
Degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field. - Strong experience working with financial crime datasets or regulated financial services environments. - Proficiency in Python (Pandas, NumPy, Scikit-Learn, PyS
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