Manager, Data Science

Company Description

Standard Bank Group is a leading financial services group focused on Africa, and an innovative player on the global stage, offering a variety of career-enhancing opportunities and the opportunity to work alongside some of the most talented and motivated professionals in the sector. Our clients range from individuals to businesses of all sizes, wealthy families, and large multinational corporates and institutions. We are passionate about fostering growth in Africa. We strive to provide true and meaningful value to our clients and the communities we serve, and to give a real sense of purpose for you.

Job Description

Supervise data mining strategies and conduct statistical analysis on large, structured, and unstructured datasets in order to understand and analyse phenomena. Model complex business problems, discover insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques. Collaborate closely with clients, data, and technology teams to transform data into critical information that can be used to make informed business decisions. Supervise predictive modelling.

Qualifications

Minimum Qualifications
Type of Qualification: Post Graduate Degree
Field of Study: Information Technology
Type of Qualification: Post Graduate Diploma
Field of Study: Information Studies

Experience Required

• Use technical knowledge and experience in the areas of ML Concepts; Software Engineering Discipline (e.g., Source Control, CI/CD); significant ML frameworks (Tensorflow, PyTorch, scikit-learn); Big Data Processing Libraries (e.g., Spark, Dask); structured and unstructured data; building complete solutions on Cloud (Azure); working with and tuning pre-existing AI Services in the Cloud; and Data Visualisation software (Power BI, etc.) to address AI and ML solution requirements.
• Deliver technical artefacts that articulate the architecture and model robustness of AI solutions, supporting the development of Technical Documentation within the area.
• Develop web applications using Python frameworks to allow businesses to interact with Machine Learning models.
• Provide automation support for ML pipelines, build code, run tests (CI), and safely roll out a new version of an application (CD) to eliminate manual errors, and provide standardised feedback loops to enable fast product iterations.
• Apply new methods and technologies to business problems and solve them in novel and creative ways to provide better insight, accuracy, and consistency.
• Investigate and implement the latest large language models to improve the Virtual risk manager (Chatbots): allow the virtual risk manager to be used as a channel to deliver risk solutions.
• Collaborate with business stakeholders to identify new AI initiatives. Conduct rapid EDA / prototyping exercises to help size projects and define success criteria at a high level, meeting the project pipeline requirements of the role.
• Work with internal clients to shape new AI projects, meeting identified client needs.
• Present relevant content at architecture, technical committees and to business stakeholders.
• Create Solution Designs and Architectures for review / verification, and reusable solution patterns for use in other projects, supporting the enablement of other team members.
• Discuss requirements with the user and collaborate with the wider team to contribute to the development of a user-centred solution that aligns with business goals, ensuring the solution is fit for purpose.
• Carry out research into and execute AI and ML development processes to ensure the business unit fulfils the requirements of AI and ML strategies.
• Translate conceptual AI needs (non-technical) into defined problem statements that can be scientifically measured to support these needs.
• Apply engineering and mathematical skills to analyse and prepare structured/unstructured data for modelling, supporting the Data Analysis and Engineering requirements of the role.
• Apply the most appropriate algorithms and/or construct new algorithms/techniques to fit the identified problem statement, supporting AI Modelling; leverage pre-built AI capabilities offered by Cloud vendors where applicable to accelerate project velocity, utilise the latest commercialised AI solutions and reduce internal technical debt.
• Integrate AI models into software that meets the requirements of the upstream or downstream system (i.e., Engineering Batch, Streaming or API based ML pipelines), supporting the solutioning of the team.

Additional Information

Behavioral Competencies:

• Taking Practical Approaches
• Communicating Information
• Challenging Ideas
• Checking Details
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Deadlines
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

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