Senior Data Scientist - FST - Finstab at South African Reserve Bank

Brief description

• The main purpose of this position is to leverage industry knowledge and analytical expertise to lead data analysis in support of policy and decision making aligned with the strategic mandate of the Financial Stability Department (FinStab) within the South African Reserve Bank (SARB).

Detailed description

The successful candidate will be responsible for the following key performance areas:

• Identify value driving opportunities for the application of advanced analytics in achieving the departmental mandate.
• Define, execute and take accountability for advanced analytics’ use cases which derive insights across key departmental subject areas.
• Lead the identification, sourcing and assessment of relevant structured and unstructured data for statistical modelling.
• Lead the design and development of robust statistical models which are modular, scalable, deployable, reproducible and version-controlled for use in analytics and reporting.
• Take accountability in all aspects of analytics solutioning, from scoping and hypothesis formulation to data sourcing, model selection, development, validation, deployment, maintenance, enhancement, optimisation and automation.
• Identify, manage and mitigate risks (i.e. model biases) and ensure adherence to ethics principles (fairness, privacy, transparency and accountability) with respect to data and advanced analytics modelling.
• Ensure alignment of work outputs to relevant departmental and SARB strategies and to Enterprise Information Management (EIM) and governance standards and frameworks.
• Communicate complex analytics concepts in a clear and concise manner to assist researchers, senior stakeholders and business leaders in interpreting model outputs and informing decision and policy actions.
• Ensure reusable analytics assets and knowledge transfer which allows business to fully integrate analytics assets in their business processes.
• Collaborate and proactively engage senior stakeholders from business and functional support areas across the analytics lifecycle to ensure solutions are well formulated, deployed, supported and adopted.
• Mentor and coach data scientists, data analysts and other junior team members.
• Provide guidance and training to relevant stakeholders on the application of analytical solutions and technologies.
• Keep abreast of industry best practices, techniques and technologies and to lead the implementation thereof to ensure value enhancing advanced analytics solutions.

Qualifications
To be considered for this position, candidates must be in possession of:

• an Honours degree (NQF 8) in STEM field, advanced degree strongly recommended, or an equivalent qualification;
• Data Science certificate;
• Data Analytics or visualisation certificate;.
• 8−10 years of experience in building, maintaining and optimising Data and Business Intelligence solutions of which 6−8 years of relevant work experience in building advanced analytics driven solutions as part of analytics setup in company/analytics services;
• five+ years of leading the development & deployment of advanced analytics solutions; and
• three+ years of experience in leading junior data scientists and data science projects.

Additional requirements include:

• deep technical expertise in Statistics & Machine Learning: Regressions;
• clustering techniques, timeseries techniques, bagging and boosting trees;
• ensemble models, neural networks;
• deep technical expertise in at least two of the following programming languages: Python, R, Statistical Analysis System (SAS), Scala, Structured query language (SQL);
• extensive experience with descriptive statistics and exploratory data analysis (EDA);
• extensive experience in working with large datasets in flat files, relational databases and distributed systems (Hadoop). 
• some exposure to Amazon Web Services (AWS), Microsoft Azure and/or Google Cloud Platform (GCP);
• extensive experience with visualization tools (e.g. PowerBI, SAS, Tableau, MicroStrategy);
• extensive experience in working with large volumes of structured and unstructured data and leveraging it to build Artificial Intelligence (AI)/Machine Learning (ML) solutions through end-to-end automated data pipelines; and
• extensive experience in machine learning operations (MLOps) for AI/ML model deployment and monitoring enhancements for standalone solutions or as part of larger product.         

Deadline:6th February,2026