Senior Data Scientist - FS

Roles and Responsibilities :

• Design, develop, and validate advanced data science models to improve International Money Transfer (IMT) payment screening. The role focuses on optimizing screening rules, enhancing alert quality, and developing alert triage capabilities while ensuring compliance with regulatory and risk management requirements.

Key Responsibilities
Analyze historical payment, alert, rule, and outcome data to identify optimization opportunities.
Develop and validate rule-mining models to assess rule performance and recommend enhancements.
Build and support alert triage models using machine learning and historical screening outcomes.
Evaluate model performance on highly imbalanced datasets and conduct testing/simulation of proposed changes.
Design confidence thresholds and review mechanisms for uncertain cases.
Translate business and risk requirements into analytical solutions and actionable recommendations.
Support model validation, governance reviews, production readiness, and operational handover.
Collaborate with business stakeholders, SMEs, and project teams throughout the model lifecycle.
Desirable Experience
Degree and relevant experience in Data Science, Statistics, Machine Learning, or a related quantitative discipline.
Strong analytical, problem-solving, communication, and documentation skills.
Strong hands-on experience in predictive modeling, classification, and model evaluation.
Experience working with highly imbalanced datasets and rare-event detection problems.
Strong experience in model calibration, Threshold optimisation, Constrained optimisation, Bayesian optimisation, Hyperparameter optimisation
Optimising business rules, thresholds and detection strategies while maintaining regulatory and risk controls.
Experience with sanctions screening, payment screening, entity resolution, fuzzy matching and financial crime analytics.
Strong understanding of model validation, statistical assurance and model risk governance in regulated environments
Applying explainable AI techniques such as LIME, SHAP and interpretable machine learning.
Working knowledge of Generative AI (GenAI) and Large Language Models (LLMs), with experience applying LLM-based solutions, RAG frameworks, prompt engineering, and AI agents to financial crime and risk management use cases.
Developing AI-based rule optimisation and rule mining solutions. Advance knowledge of XGBoost, LightGBM, CatBoost, Random Forest, RuleFit, Isolation Forest, and Bayesian Models

Preferred Domain Experience
Experience in payments, financial crime, sanctions screening, transaction monitoring, fraud analytics, or other highly regulated banking environments is highly desirable. The role requires balancing model performance with risk, regulatory, and operational requirements.