VACANCY DETAILS

Job particulars

Key responsibilities

Specifically, the successful jobholder will be required to:.
Collaborate with business and product teams to identify priority business challenges, define analytical approaches and establish measurable outcomes.
Analyse complex datasets to uncover trends, patterns and opportunities, translating findings into actionable insights and recommendations for business leaders and stakeholders.
Develop, train, test and validate statistical, machine learning, generative AI and optimisation models using robust and reproducible methodologies.
Establish model performance benchmarks, validation criteria and monitoring frameworks, ensuring fairness, explainability, appropriate human oversight and compliance with the Bank’s responsible AI and model risk requirements.
Work with Data Engineers and Application Engineers to deploy models and AI solutions into production using approved MLOps/LLMOps practices.
Design and conduct experiments, pilots and post-deployment evaluations to measure business impact and continuously improve model performance.
Monitor model effectiveness and business outcomes, identify performance deterioration or data drift, and recommend model recalibration, retraining or retirement where necessary.
Maintain comprehensive model documentation, code and validation evidence to support governance, audit and ongoing model management.
Contribute reusable analytical assets and share knowledge to strengthen data science and AI capabilities across the Bank.

The successful candidate will be required to have the following skills and competencies:.
A bachelor’s degree in Data Science, Statistics, Mathematics, Actuarial Science, Computer Science, Economics, Engineering, Operations Research or a related quantitative field.
At least four (4) years of relevant experience in data science, advanced analytics, machine learning or applied AI.
Demonstrated experience taking analytical or machine learning solutions from problem definition through validation to production deployment.
Proficiency in Python or R and SQL, with strong knowledge of statistical modelling, machine learning, feature engineering and model evaluation.
Experience in credit, fraud, collections, customer analytics, banking operations or a related business domain will be an added advantage.
A relevant master’s degree or professional certification in data science, machine learning, cloud computing or AI will be an added advantage.

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