Decision Scientist III

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)

Knowledge and Experience

Minimum Experience:

• NB. Length of relevant work experience required is conditional on the qualifications obtained 
• Deep understanding of and expert experience in state of the art statistical (predictive and classification) model development and deployment principles and techniques incl. 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.)
• General business know-how / acumen: (e.g., risk, compliance, operations; e.g., NCR, POPIA, SARB)
• Business analysis and requirements gathering
• Working in cloud environments (e.g., Azure, AWS, and large relational databases) 
• Experience in at least two Machine Learning languages (e.g., Python or SAS Viya) 
• Expert functional business area (e.g., Credit) environment knowledge and experience

Minimum Knowledge:

• Deep understanding of state of the art statistical (predictive and classification) model development and deployment principles and techniques incl. 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.) and can teach to a broad and wide technical audience
• Underlying theory and application of machine learning models; able to understand underlying principles and theory, and able to teach others
• Best practices for decision science (such as reusability, reproducibility, continuous monitoring, etc.)
• Technical understanding

Ideal Knowledge and Experience:

• Over 6 years’ experience in an analytical science role
• Financial sector experience
• Retail credit environment / industry (credit cycle)
• Working with multiple teams to deliver predictive models into a production environment
• Stakeholder relationship engagement and management at Senior Management and Executive levels
• Capitec Decision Science lifecycle
• Project management methodologies

Skills

Additional Information