Data Scientist

At the heart of the financial services industry lies the ability to transform raw numbers into meaningful strategies. This role within the Standard Bank Group offers a unique opportunity to sit at the intersection of advanced analytics and practical business solutions. You will be responsible for digging into complex datasets to identify trends, mitigate risk, and uncover hidden opportunities that drive the organisation’s growth across the African continent.

About the role

This position is designed for a data-driven professional who is comfortable navigating both structured and unstructured data environments. You will function as a bridge between technical data architecture and high-level business strategy. By leveraging sophisticated machine learning models and statistical frameworks, you will provide the insights necessary for the bank to make informed decisions that impact clients ranging from private individuals to large multinational corporations.

Success in this role requires a blend of rigorous technical ability and the communicative skills needed to present findings to non-technical stakeholders. You will work within a collaborative environment, engaging with technology teams to ensure that data flows are efficient and that the insights you generate are both actionable and scalable.

Your daily contributions will involve applying data mining techniques to extract patterns from large-scale data repositories. Key responsibilities include:

• Developing and deploying predictive models, including credit scoring, propensity analysis, and churn prediction.
• Designing and implementing algorithms to process complex, unstructured data streams, including imagery and real-time data.
• Collaborating with cross-functional teams to refine data architecture, ETL processes, and database integrity.
• Utilising visualisation tools like Power BI or Tableau to turn complex datasets into clear, narrative-driven dashboards.
• Managing project timelines to ensure that technical outputs meet the rigorous demands of the financial services sector.
• Interpreting data to challenge existing business assumptions and exploring new possibilities for data monetisation.

What they're looking for

The ideal candidate possesses a strong technical foundation backed by 3 to 4 years of relevant experience in the data analytics space. You should be familiar with the nuances of financial data and possess the following qualifications:

• **Education:** A relevant first degree in Information Technology or a related field.
• **Technical Toolkit:** Proficiency in data science software such as R, SAS, or SPSS, alongside a strong grasp of software engineering principles.
• **Data Expertise:** Demonstrated ability to manage data flows, ETL pipelines, and technical business intelligence tasks.
• **Analytical Rigour:** Experience in building statistical models and a deep understanding of data mining methodologies.
• **Soft Skills:** Strong behavioural competencies, including the ability to articulate complex information clearly, work effectively within a team, and meet strict project deadlines.

Data Science roles within major South African financial institutions are highly sought after, reflecting the ongoing digital transformation of the banking sector. Professionals in these roles often progress into senior data architecture or management positions, with compensation packages in the local market generally reflecting a premium for those with specific experience in credit scoring and propensity modelling. Given the scale of operations at a group like this, the role offers significant exposure to international best practices, making it an excellent platform for career growth in the fintech or banking sectors.

Type of Qualification: First Degree
Field of Study: Information Technology

Experience Required
Data Monetisation
Data & Analytics
3-4 years
Experience in working with unstructured data (e.g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, SPSS, etc. Experience with data visualisation tools, such as Power BI, Tableau, etc.

3-4 years
Proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience. Experience in building models (credit scoring, propensity models, churn, etc.)

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Things
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering