Data Analyst - Credit Optimisation

The Opportunity

JOB DESCRIPTION

A challenging opportunity is available within the Credit Optimisation department of TFG Financial Services. We are looking for a dynamic, highly motivated individual to develop analytical solutions to business problems using analytical techniques and tools. The successful candidate will be part of a team of data scientists that develops predictive and prescriptive (mathematical optimisation) models to drive credit risk decisioning throughout the business.

Job Description

This will involve (but is not limited to):

1. Develop predictive models that enable mathematical optimisation to find an optimal solution within the business constraints
2. Assist with the development and maintenance of mathematical optimisation solutions to support critical decisioning in credit business
3. Ensure appropriate statistical methodology and data mining / analytical techniques are used in the modelling process to deliver and deploy robust and effective models
4. Research and implement relevant and new machine learning techniques
5. Extract data accurately and timeously for modelling and optimisation
6. Develop and maintain Analytics Based Tables (Credit ABTs) to improve the accuracy of predictive models
7. Derive business insights by leveraging traditional and alternative data sources
8. Support model and strategy implementation, testing and monitoring
9. Compile documentation of analytical processes and results, adhering to agreed documentation standards
10. Effectively communicate and present analytical results to different stakeholders

To Take Up This Position You Should Have

1. 3+ Years’ experience in an analytical/data scientist position focusing on Predictive and Prescriptive analytics is essential
2. Honours or preferably Master’s degree in mathematics and/or Statistics including subjects specifically on mathematical optimisation (linear programming / mathematical programming) will be highly advantageous
3. Experience in using data analysis software packages (SQL, SAS, R, Python, FICO Analytics Workbench). This includes intermediate to advanced code writing skills in one or more of these languages
4. Experience in formulating mathematical optimisation problems (SAS Proc Opt model for example)
5. Experience with data mining and machine learning techniques such as optimisation, logistic regression, linear regression, SVM, decision trees, K-means, cluster analysis etc.
6. Previous modelling experience in retail credit will be advantageous.
7. Good strategic and conceptual abilities
8. Excellent data analysis, analytical and problem-solving skills
9. High attention to detail
10. Excellent documentation and verbal communication skills
11. Good time management skills

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