Credit Risk Data Analyst - Parow at TFG (The Foschini Group)

Position Description:

• An exciting opportunity has arisen within the Credit Department of TFG Financial Services for a curious, motivated individual to drive strategies, provide analytical support and to quantify strategic initiatives in order to drive healthy growth on the TFG credit book, while growing and building a career in data and credit analytics.

Job description

• This role is focused on supporting credit initiatives from a portfolio growth perspective.
• Develop strategies to grow new accounts through outbound marketing/social media and other channels
• Analysing and creating insight from information and historical trends
• Engage with relevant stakeholders to obtain key necessary information to explain data observations
• Providing analytical solutions to complex business problems
• Documentation of analytical processes and results
• Effective communication and presentation of analytical results and credit-related topics to peers and relevant stakeholders
• Projecting/forecasting of business trends and targets based on historic events
• Build profitability models for proposed initiatives
• Data mining of credit bureau and other external data to provide insights and trends
• Extraction and preparation of data for internal development projects as well as outsourced solutions
• Grow into a senior data scientist and get exposure to other areas of the credit lifecycle, like account management, collections and recoveries.

To take up this challenging position you should have:

• Data interpretation skills
• Good conceptual and strategic abilities
• Problem solving, judgment and self-management skills A degree in a numerate discipline, preferably Statistics / Mathematics / Operations Research / Engineering (Honours / Masters degree preferable)
• Excel experience
• Knowledge and experience in statistical/data management software packages (SAS/SQL) would be advantageous
• Knowledge of the credit industry and credit life cycle management is preferred Hands-on experience of large-scale customer database data interrogation and manipulation would be advantageous
• Experience with data mining and statistical techniques such as logistic regression, decision trees, cluster analysis etc. is preferred