Data Scientist - Credit Optimisation at The Foschini Group

About the position

Package & Remuneration
JOB DESCRIPTION

The opportunity:

A challenging opportunity is available within the Credit optimisation and VAS analytics department of TFG Financial Services. We are looking for a dynamic, highly motivated individual to develop analytical solutions to business problems using state of the art techniques. The successful applicant will develop and oversee the VAS take-up strategy using descriptive and predictive analytics while working closely with the VAS business stakeholders

Job Description:

This will involve (but is not limited to):

• Develop, maintain, and track predictive models which are used in VAS campaigns to select the appropriate leads to maximise profit
• Assist in the development of forecasting models using various statistical modelling and machine learning techniques
• Provide analytical support to the VAS business stakeholders by translating business problem into analytical problem
• Derive business insight from analysing data and effectively present results
• Manage relationship with stakeholders and communicating effectively
• Ensure appropriate statistical methodology and data mining / analytical techniques are used in the modelling process to deliver and deploy robust and effective models
• Extract data accurately and timeously - proficient in coding in SAS or SQL and experience with working with complex database structures
• Documentation of strategy (as well as test results), adhering to agreed documentation standards
• Appropriately engage with internal and external stakeholders to improve the operating level of the portfolio
• Research and implement contemporary machine learning techniques

To take up this challenging position you should have:

• An honours degree in Mathematics or Statistics
• At least 3-5 years' experience in an analytical position providing predictive and prescriptive solutions to business
• Experience in using data analysis software packages (SQL, SAS, R, Python)
• Experience with machine learning techniques such as optimisation, logistic regression, linear regression, decision trees, K-means, cluster analysis etc
• Good strategic and conceptual abilities
• Excellent data analysis, analytical and problem-solving skills
• Forecasting and reporting experience
• High attention to detail
• Excellent documentation and verbal communication skills

Preference will be given, but not limited to, candidates from designated groups in terms of the Employment Equity Act.