Senior Decision Scientist

We are looking for a Senior Decision Scientist to strengthen our credit decisioning and model-oversight capability across markets and products. The successful candidate will develop model-informed decisioning tools and analytical solutions and lead the independent validation, calibration, implementation assurance and ongoing monitoring of credit risk models and decision strategies. Working closely with Data Science, which leads the development and retraining of core predictive models, the Senior Decision Scientist will ensure that models are robust, appropriate for their intended use and translated into stable, controlled credit decisions. This is a senior individual-contributor role requiring strong quantitative judgement, hands-on analytical expertise and the ability to translate complex findings into clear recommendations for senior and C-suite stakeholders.

• Develop reusable analytical tools, simulations and frameworks to support credit policy, eligibility, affordability, risk appetite, decision thresholds and segmentation.
• Lead the independent end-to-end validation of new and existing credit risk and decisioning models, including material model changes.
• Evaluate model data, target definitions, methodology, feature logic, assumptions, limitations and suitability for intended use.
• Assess model performance, calibration, stability and robustness across markets, customer segments and time periods.
• Design and maintain calibration, probability-mapping and risk-segmentation frameworks that translate model outputs into coherent risk bands and decision strategies.
• Conduct scenario and sensitivity analysis to understand the impact of proposed changes on approval rates, risk, exposure and expected portfolio performance.
• Define requirements, acceptance criteria and controls for model and decisioning changes, validating the process from source data through to production decisions.
• Reconcile analytical results with production outputs and conduct post-implementation reviews to identify unintended impacts or implementation issues.
• Develop and maintain monitoring frameworks covering model performance, calibration, data quality, applicant profiles, decision outcomes and portfolio performance.
• Diagnose performance deterioration or unexpected behaviour, distinguishing between model, data, implementation and portfolio drivers.
• Partner with Credit Risk, Pricing, Data Science, Data, Engineering, Finance and Commercial teams to implement improvements safely and effectively.
• Present findings, effective challenge and recommendations clearly to technical, non-technical and C-suite stakeholders.

Essential:
• Strong experience in decision science, data science, credit risk analytics, model validation or a related quantitative role.
• Strong knowledge of statistics and data science, with sufficient modelling expertise to reproduce, evaluate and independently challenge predictive models.
• Advanced SQL skills for data extraction, validation, reconciliation and performance analysis.
• Advanced Python skills for statistical analysis, simulation, model validation, monitoring and the development of analytical tools.
• Strong understanding of the model lifecycle, including development methodology, evaluation, validation, calibration, implementation and ongoing monitoring.
• Experience evaluating model performance using appropriate techniques, including out-of-sample and out-of-time testing, stability analysis, benchmarking and sensitivity testing.
• Strong quantitative and deductive reasoning, with the ability to investigate complex issues independently and identify practical solutions.
• Clear written and verbal communication, including the ability to explain technical findings and recommendations to non-technical and senior stakeholders.
• Ability to manage competing priorities and deliver high-quality work in a fast-paced environment.

Desirable:
• Experience in SME lending, commercial credit or embedded finance.
• Experience independently validating or developing credit risk or decisioning models.
• Experience implementing, testing or monitoring models in a production decisioning environment.
• Experience developing credit-strategy tools, simulations, calibration frameworks or monitoring solutions.
• Familiarity with model-governance or formal model-approval processes.
• Experience working across multiple international markets or portfolios.
• Experience using transactional, open-banking or other alternative data in credit-risk analysis.