About the role:

As the Manager: Data Science/AI at Capitec, you will lead the bank’s AI and advanced analytics initiatives, driving innovation and enabling data-driven decision-making across the organization. Your role will focus on defining and implementing a forward-thinking AI strategy while fostering a high-performing team of data scientists to deliver impactful AI/ML solutions aligned with business objectives.

Purpose Statement

• To solve business problems, create new products and services, and improve processes through using the disciplines of data science, quantitative (financial) analysis, and traditional scoring techniques – translating active business data into usable strategic information.
• To look at ways of analysing and optimising data as it relates to a specific business area; framing data analysis in terms of the decision-making process for questions or business problems posed by a stakeholder.
• To help build and deliver Capitec’s AI strategy, enabling data-led and improved business decision making.
• To design quantitative advanced analytics models that answer business questions and/or discover opportunities for improvement, increased revenue, or reduced costs.

Education (Minimum)

Education (Ideal or Preferred)

Experience and Knowledge

Minimum Experience and Knowledge:

• Length of experience required is also conditional on qualifications obtained
• Statistical (predictive and classification) model development and deployment principles and techniques; including traditional scoring (logistic regression with binning and missing value replacement, e.g., reject inference), Machine Learning (neural networks, SVM, random forests, etc.), and quantitative analysis (time value of money, etc.)
• At least one Machine Learning language (e.g., Python or SAS Viya)
• Business analysis and requirements gathering
• General business know-how (e.g., risk, compliance, operations – such as NCR, POPIA, and SARB)
• Cloud environments (e.g., Azure, AWS, and large relational databases)
• Functional business area (e.g., Credit) environment knowledge and experience
• Developing scorecards from scratch
• Underlying theory and application of machine learning models
• Best practices for decision science (such as reusability, reproducibility, continuous monitoring, etc.)

Ideal Experience and Knowledge:

• Over 5 years’ experience in an analytical science role
• Working with multiple teams to deliver predictive models into a production environment
• Financial sector
• Credit environment / industry (Credit cycle)
• Bank decision science lifecycle

Skills

Additional Information