Senior Data Scientist (A)

Tasks

• Redesign the existing client risk rating framework from a categorical (Low/Medium/High) model to a continuous or tiered numerical scoring system
• Develop a points-based risk scoring engine based on established credit risk and fraud detection methodologies
• Define risk factors and allocate scoring weights across KYC and client-related attributes according to their relative risk contribution
• Apply interpretable statistical and machine learning techniques suitable for regulated environments, including logistic regression with WOE and explainable tree-based models
• Define, calibrate, and validate scoring bands, thresholds, and breakpoints
• Build reproducible Python-based pipelines for score calculation, backtesting, validation, and sensitivity analysis
• Perform feature engineering and model development using production-representative data within a secure sandbox environment
• Ensure complete model lifecycle ownership, including business requirements gathering, development, validation, documentation, monitoring, deployment support, and maintenance
• Collaborate closely with compliance, financial crime, analytics, and transformation stakeholders
• Deliver comprehensive documentation and ensure knowledge transfer to downstream teams

Requirements

• Completed degree (Master’s degree or PhD) in Quantitative Finance, Mathematics, Physics, Engineering, or a related quantitative discipline
• Minimum 7 years of professional experience in quantitative analytics, data science, or statistical modelling within leading banks, asset managers, capital markets firms, or fintech organisations
• Advanced expertise in Python, including object-oriented programming, modular software architecture, exception handling, performance optimisation, and test-driven development
• Extensive hands-on experience with pandas, NumPy, SciPy, scikit-learn, statsmodels, and either PyTorch or TensorFlow
• Proven experience developing reusable Python packages, libraries, and production-grade analytical solutions
• Strong knowledge of risk scorecard development, model validation, and explainable machine learning approaches
• Experience building scoring frameworks and risk models in regulated financial services environments
• Excellent SQL skills with proven experience writing and optimising complex queries on large Oracle databases
• Demonstrated expertise across the complete model lifecycle, from requirements gathering through deployment and monitoring
• Strong analytical mindset with a proven track record of improving model robustness, identifying flawed assumptions, and preventing biased or misleading conclusions
• Language skills: Business fluent English (C1/C2) and basic 

Soft Skills

• Outstanding communication and stakeholder management skills
• Ability to translate complex technical concepts into clear business insights and recommendations
• Strong problem-solving mindset with a structured, analytical, and quality-focused approach