Overview
Decision Scientist I Jobs in Stellenbosch, Western Cape, South Africa at Capitec
Title: Decision Scientist I
Company: Capitec
Location: Stellenbosch, Western Cape, South Africa
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)
Honours Degree in Mathematics or Statistics
Education (Ideal Or Preferred)
Masters Degree in Mathematics or Statistics
Knowledge and Experience
Experience
Minimum Experience and Knowledge:
NB. 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.)
Basic 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)
Knowledge
Basic general business know-how: e.g. risk, compliance, operations e.g. NCR, POPIA, SARB
Basic functional business area (e.g. Credit) environment knowledge and experience
Understanding of 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.).
Relational database technologies
Ideal Experience
Financial sector experience
Skills
Planning, organising and coordination skills
Numerical Reasoning skills
Attention to Detail
Problem solving skills
Decision making skills
Interpersonal & Relationship management Skills
Analytical Skills
Researching skills
Additional Information
Clear criminal and credit record
