KEY DUTIES AND RESPONSIBILITIES
- Design and implement advanced data analytics frameworks for macroeconomic forecasting, payments monitoring, supervisory technology (SupTech) and financial inclusion insights.
- Lead the exploration, cleaning, integration, and analysis of structured and unstructured data sources (e.g., payments, mobile money, banking systems, fintech sandbox data).
- Develop predictive and prescriptive models to support risk management, fraud detection and resilience analysis.
- Build, train, validate, and deploy AI/ML models for applications such as anomaly detection, natural language processing (NLP), chatbot assistants and automation of regulatory reporting.
- Champion the adoption of machine learning pipelines, MLOps practices and model governance within the organisation.
- Ensure explainability, fairness and compliance of AI models in line with emerging AI governance frameworks(e.g., ISO/IEC 42001, OECD AI Principles).
- Contribute to the organisation’s Digital Transformation Strategy and Fintech Innovation Hub initiatives by providing technical leadership on data-driven innovation.
- Explore emerging technologies such as agentic AI, big data platforms, and advanced analytics for the organisation.
- Support fintech sandbox evaluations through data-driven assessments of participant solutions.
- Develop and enforce AI/ML governance frameworks covering ethical use, bias detection, transparency, and accountability.
- Ensure compliance with data privacy, cybersecurity, and business continuity standards in all data science initiatives.
- Contribute to the organisation’s participation in regional and international working groups on data, AI, and fintech (e.g., SADC, BIS, IMF).
- Mentor and upskill junior analysts, data engineers, and project teams in data science and AI/ML methods.
- Engage with academic institutions, fintechs, regulators and innovation hubs to advance applied research and collaboration.
- Prepare executive briefs, dashboards and reports on insights from data science and AI/ML initiatives.
QUALIFICATIONS AND EXPERIENCE
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, or related field.
- Master’s degree in Data Science, AI/ML or a quantitative discipline is an added advantage.
- Professional certifications in data science, AI/ML, or big data platforms (e.g., TensorFlow, PyTorch, AWS/Azure/GCP ML) are desirable.
- At least 5 years’ experience in developing and implementing data science and AI/ML framework, with demonstrated leadership experience in analytics projects.