Purpose Statement

To solve business problems, create new products and services and improve processes through using the disciplinces of data science, translating active business data into usable strategic information.
To look at ways of analyzing 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/Machine Learning 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. 

Candidate Requirements
Qualifications & Experience:

Degree in Mathematics | Statistics | Data Science | Business Analytics | Engineering | Computer Science Plus 4+yrs relevant work experience. OR
Hons Degree in Mathematics | Statistics | Data Science | Business Analytics | Engineering | Computer Science Plus 3+yrs relevant work experience. OR
Masters Degree in Data Science or relevant discipline e.g. Mathematics, Statistics, Engineering, Computer Science.

Knowledge:

Functional business area (e.g. Credit) environment knowledge and experience
Regulatory requirements e.g. NCR, POPIA, SARB 
Business analysis, requirements gathering, translating into business requirement specifications and designing and delivering business solutions.
With modern software development best practices.
Working in cloud environments, e.g. Azure, AWS
Different operating systems / databases / programming language
Predictive modelling techniques (statistical and machine learning) and deployment 
Extracting, aggregating, cleaning and analysing data from large relational databases
Data Science lifecycle and applicable skills within 

Functional Skills:

Programming; Data base manipulation | Data Exploration Visualisation
Statistical model development
Advanced Computer literacy and programming (Office Suite, Working on different OS environments, e.g. working on remote Linux environment and dockerising machine learning models)
Building and deployment of models
ML language, for example SQL SAS
Version Control
Ability to productionalise and solution design
Ability to learn, understand and apply complex machine learning methodologies
Functional domain skill of the (e.g. credit) environment
Capability to identify and define and solve the business problem i.e. application of domain know how and problem solving.
Cloud computing (navigating on cloud vs on prem.)