Job Description

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector’s most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. We’re passionate about creating growth in Africa. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you.

For us, data is not just an asset; it’s the heartbeat of innovation and progress, fuelling our ability to drive growth on the continent we proudly call home.

In an era where our world is shaped by data-led solutions, we’re at the forefront of building this critical function in our business. Our Data Science Graduate Programme puts you right at the centre of this fast-moving area, with a focus on structured training and development and a commitment to technical, personal, and professional excellence.

So, who are we looking for? You’ll be hugely talented, with a strong quantitative background. You’ll also be obsessed with solving real client problems by analysing data, identifying insights, and building bespoke solutions. Bring these skills to our graduate programme, and discover how - through learning, support, and unrivalled exposure - you’ll transform your potential into impact.

Here, your ambition and skill will find its match in our dedication to excellence.

Key Responsibilities:

Client

• Supports business integration through integrating model outputs into end-point production systems, incorporating business requirements and knowledge of best practices.

Data

• Assist the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals. Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation, and derivation of new variables/features. Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders.
• Assists various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Assists analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives.
• Codes, tests, and maintains scientific models and algorithms and identifies trends, patterns, and discrepancies in data and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
• Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches. Communicate data information to business with respective stakeholders, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations under the supervision of data scientists.
• Utilises the appropriate data storage and data mining tools to ensure value can be extracted from the sourced data. Mines data using state-of-the-art methods and enhances data collection procedures to include information that is relevant for building models.

People

• Liaise and collaborate with the Data Science Guild providing support to stakeholders in the department for its data centric needs. Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining/science outcomes. Presents findings and observations to team for development of recommendations.

Product

• Supports various mathematical, statistical, and simulation techniques to answer business questions within specific areas of focus. Develops modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Supporting reporting production ensuring sustainable and effective modelling solutions.
• Supports and implements operational IA plan, rules, methodologies, and coding initiatives in order to ensure IA for remediation efforts. Support and implements the strategy for productionalising automation software so that it is accurate and well maintained.

Technology & Architecture

• Assists in building machine learning models from and utilises distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.

Ready? Apply for the Standard Bank Data Science Graduate Programme below.

Application closing date: 31 May 2026

Qualifications

Completed or currently enrolled in an Honours or Master’s degree in the following fields:

• Data Science / Analytics
• Statistics
• Computer Science
• Applied Mathematics
• Quantitative Risk Management
• Engineering (Computer, Industrial, Electronic, Electrical, Mechatronic, Mechanical, Chemical)

Honours (or an equivalent 4-year degree), to be completed by 31 December 2026.

Additional Information

Minimum requirements:

• Must be a South African citizen.
• Should you have work experience, it should not exceed 24 months
• Minimum academic average of 70% for the 3rd year of undergraduate studies.
• Minimum academic average of 65% for Honours (or the 4th year of an equivalent 4‑year degree) or Master’s studies.
• Matric certificate to be submitted with application.
• Full academic transcripts to be submitted with application for undergraduate and postgraduate studies.
• NB: To ensure efficient processing of your application, please submit original electronic PDF (non‑scanned) versions of your documents.