Lead Data Scientist

A leading enterprise organisation is seeking a senior-level Data Scientist to lead the development, validation, and implementation of advanced quantitative models in a highly regulated environment. This role will serve as a technical leader, partnering with business and executive stakeholders to identify financial opportunities, quantify outcomes, and drive data-driven decision making. The successful candidate will bring deep expertise in risk and financial modelling, strong statistical and mathematical foundations, and the ability to independently deliver complex analytics projects from concept through deployment and governance review.

• Design, develop, validate, and monitor advanced predictive and risk models.
• Analyse large-scale financial, transactional, and customer datasets.
• Apply machine learning and statistical techniques to solve complex business problems.
• Translate model insights into measurable financial impact and business recommendations.
• Lead analytics initiatives from problem definition through implementation and performance tracking.
• Present technical findings and strategic recommendations to senior leadership and executive stakeholders.
• Support model governance activities, regulatory reviews, and external audit processes.
• Collaborate with cross-functional teams to ensure models are aligned with business objectives and compliance requirements.
• Provide technical guidance and mentorship to peers and junior team members.

• 8+ years of experience in Data Science, Quantitative Analytics, Risk Analytics, or a related discipline.
• Advanced proficiency in Python and SQL.
• Strong expertise in data modelling, statistical analysis, and quantitative problem solving.
• Experience developing and evaluating gradient boosting and other predictive modelling techniques.
• Proven ability to independently own and drive complex analytical projects.
• Exceptional communication and presentation skills, including experience presenting to executive audiences.
• Demonstrated experience defending models during audits, reviews, or governance assessments.
• Background in financial or risk modelling within a regulated industry.

Candidates should have experience in one or more of the following areas:

• Credit risk modelling
• Financial forecasting and modelling
• Loss estimation and assessment
• Probability of Default (PD) modelling
• Risk scorecard development
• Payment and behavioural modelling
• Credit attribute modelling
• Banking, lending, consumer credit, mortgage, or credit bureau data
• Financial impact analysis
• Risk analytics within regulated financial institutions

• Bachelor's degree in a quantitative discipline such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field.
• Master's degree in Data Science, Statistics, Applied Mathematics, Economics, or a similar quantitative discipline preferred.
• Advanced academic training with significant mathematical and statistical coursework.

• Hybrid work arrangement requiring onsite attendance four days per week (Monday through Thursday).