Lead, Machine Learning

This role will be reporting to the Head of AI Strategy and Adoption. The role sits within the CDO office of the Bank, and is critical in driving AI adoption and enablement across the Bank's functions. The aim of this role is to deploy as one of the members of the Function Squads that will be focused on FinCrime. Our Ideal Candidate 10+ years experience in building and deploying machine learning models to detect and prevent financial crimes, achieving a measurable reduction in fraud losses for Standard Chartered. Implement Generative AI solutions to generate synthetic data for training, improving model accuracy by at least 15% in FinCrime and Compliance use cases. Mentor and guide a team of machine learning engineers and data scientists, fostering a culture of technical excellence and innovation within the FinCrime and Compliance domain. Lead a team to deliver production-ready ML pipelines within 6 months, integrating real-time transaction monitoring for anti-money laundering (AML) compliance. Optimize feature engineering and model tuning to increase detection rates of suspicious activities Roll out scalable ML systems that process millions of transactions daily, reducing false positives in compliance alerts Drive the adoption of advanced ML algorithms to identify sanctions evasion patterns, cutting investigation times for compliance teams Deliver quarterly reports showing improvements in key FinCrime KPIs, such as alert accuracy and case resolution speed, to senior management. Execute A/B testing on ML-driven compliance tools, achieving a statistically significant boost in operational efficiency Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) to build and deploy production-grade machine learning models. Hands-on experience with LLMs techniques (e.g., RAG, Agents, Graph RAG) to improving FinCrime detection accuracy. Expertise in model optimization and hyperparameter tuning to achieve high precision and recall in fraud detection and compliance applications. Advanced understanding of statistical methods and anomaly detection algorithms to identify suspicious patterns in financial data.

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