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For one of our clients, we are seeking an experienced Senior Data Scientist (A) to support the redesign and transformation of a Client Risk Rating (CRR) model within a highly regulated financial services environment. This assignment focuses on developing a transparent, explainable, and statistically robust risk scoring framework that supports both automated decision-making and human oversight across compliance and financial crime processes.
Duration of employment
01.09.2026
- 31.12.2026
Pensum
80%
Reference
3953
Description
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