Decision Scientist

About

Description

To leverage data science, quantitative analysis, and machine learning to solve complex business problems, optimize processes, and develop strategic insights. You will design advanced analytics models to enhance revenue, reduce costs, and support Capitec’s AI strategy while collaborating with stakeholders to translate data into actionable business intelligence

Responsibilities

• 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

Qualifications

• Education (Minimum) Honours Degree in Mathematics or Statistics Grade 12 National Certificate / Vocational Education (Ideal or Preferred) Masters Degree in Mathematics or Statistics 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 eg reject inference), Machine Learning (neural networks, SVM, random forests, etc), and quantitative analysis (time value of money, etc) At least one Machine Learning language (eg, Python or SAS Viya) Business analysis and requirements gathering General business know-how (eg, risk, compliance, operations - such as NCR, POPIA, and SARB) Cloud environments (eg, Azure, AWS, and large relational databases) Functional business area (eg, 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 Retail credit environment / industry (Credit cycle) Bank decision science lifecycle

Application instructions:

• Click on the link to apply