Manager, Data Science (Machine Learning) at Standard Bank Group

JOB DESCRIPTION

• To assist with advanced analytics and deep insight by being a proactive partner in providing customer centric data analytics, including alternative methods of aggregating raw data (internal and external), which will ultimately influence the way in which we view and act on customer behaviour and customer health, i.e. identifying risks and opportunities. Implementing the use of machine learning to challenge and improve predictive modelling techniques, the available characteristic universe across the customer life cycle and optimising segmentation to enhance model performance. Solutions should satisfy customer centricity and digitisation objectives. Including but not limited to Extracting meaningful insights from data, Predictive modelling and machine learning, Stakeholder Engagement, Leadership and People Management.

QUALIFICATIONS

Minimum Qualifications

• Type of Qualification: Post Graduate Degree
• Field of Study: Mathematical Sciences
• Other Minimum Qualifications, Certifications or Professional Memberships: Honours degree (with majors in Statistics / Applied Mathematics / Econometrics / Actuarial Sciences/Engineering.

Experience Required

• 5-7 Years experience in advanced analytics, combined with sufficient knowledge of products and customer behaviour in a financial services environment.
• Some experience in managing a quantitative team at a junior management level. Experience with data mining and retail credit risk modelling.
• Communication skills, in particular, communication of technical concepts to a non-technical audience.
• Team management and leadership experience as well as interaction at executive level. Coding ability and experience to deal with big datasets (preferably in SAS, R, Python or SQL). 
• Some experience of machine learning modelling techniques, with an understanding of underlying details and parameters.  Good analytical problem-solving skills.
• Experience in developing machine learning or AI models.
• Experience with container technologies (e.g. Docker, Kubernetes)
• Experience with cloud-based infrastructures Azure and/or AWS with their MLOps tools.

ADDITIONAL INFORMATION

Behavioural Competencies:

• Generating Ideas
• Exploring Possibilities
• Challenging Ideas
• Examining Information
• Developing Practical Approaches

Technical Competencies:

• Scientific Reasoning (ScR)
• Statistical Inference
• Data Analysis
• Data Integrity
• Strategic Planning and Reporting