(907) Data Scientist - FinStab at South African Reserve Bank

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.
• Execute advanced analytics use cases to derive insights across key departmental subject areas.
• Identify, source and assess relevant structured and unstructured data for statistical modelling.
• Prepare data for statistical modelling by identifying data correlations and collinearity and performing required feature engineering.
• Develop robust statistical models that are modular, scalable, deployable, reproducible and versioned for analytics and reporting purposes and ensure that models are validated and meet required performance thresholds.
• Fully document analytics use cases according to best practice standards (including documenting model selection, validation, algorithms and code).
• Monitor, measure and report on analytical results to ensure appropriate business recommendations and insights. The analytical results may require the decision requirements model to be updated.
• Deploy, maintain, enhance, optimise and automate analytics solutions.
• Identify and mitigate risks (i.e. model biases) and ensure adherence to ethics principles (fairness, privacy, transparency and accountability) with regard to data and advanced analytics modelling.
• Align work outputs to relevant departmental and South African Reserve Bank strategies, and enterprise information management and governance standards and frameworks.
• Communicate complex analytics concepts in a clear and concise manner to assist researchers and stakeholders in interpreting model outputs and informing decisions and policy actions.
• Collaborate and proactively engage stakeholders from business and functional support areas across the analytics life cycle to ensure solutions are well formulated, deployed, supported and adopted.
• Keep abreast of industry best practices, techniques and technologies and lead the implementation thereof to ensure value-enhancing advanced analytics solutions.

Qualifications

Job requirements

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

• a Bachelor’s degree (NQF 7) in the science, technology, engineering and mathematics (STEM) field or an equivalent qualification;
• data science certification;
• data analytics or visualisation certification; 
• six to eight years’ experience in building, maintaining and optimising data and business intelligence solutions;
• at least three to five years’ experience in building advanced analytics solutions as part of the analytics setup in a company/analytics service provider; and
• a minimum of two years’ experience in deploying, maintaining and optimising advanced analytics solutions.
• an advanced degree.

The following would be an added advantage:

Additional requirements include:

• technical expertise in at least two of the following programming languages: Python, R, SAS, Scala and SQL;
• technical expertise in statistics and machine learning (ML) – regressions, clustering and time series techniques, bagging and boosting trees, ensemble models and neural networks;

working experience in:

• descriptive statistics and exploratory data analysis (EDA);
• large datasets in flat files, relational databases and distributed systems (Hadoop), with some exposure to AWS, Azure and/or GCP desirable;
• visualisation tools (e.g. PowerBI, SAS, Tableau and MicroStrategy);
• leveraging a large volume of structured and unstructured data to build artificial intelligence (AI)/ML solutions through end-to-end automated data pipelines;
• MLOps for AI/ML model deployment monitoring/enhancements for standalone solutions or as part of larger products;
• the ability to communicate complex ideas effectively, both verbally and in writing; and
• experience in working and collaborating with a variety of stakeholders throughout a data science project’s life cycle.