Decision Scientist II

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. 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 Knowledge and Experience:
Experience:

• Length of experience required is conditional on the qualifications obtained 
• Experience in statistical (predictive and classification) model development and deployment 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: 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 one ML language (e.g. Python or SAS Viya) 
• Functional business area (e.g. Credit) environment knowledge and experience

Knowledge:

• 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.).
• Underlying theory and application of machine learning models; able to understand underlying principles and theory. 
• Best practices for decision science such as reusability, reproducibility, continuous monitoring, etc

Ideal Knowledge and Experience:

• Financial sector experience
• Working with multiple teams to deliver predictive models into a production environment
• Capitec Decision Science lifecycle 

Skills

Additional Information