Job description:
The development and deployment of a suite of response, propensity and segmentation models to improve customer targeting capability for acquisition, cross-selling, retention and customer management initiatives across all retail brands, financial products and delivery channels.
Analysing and reporting on customer behavioural patterns and proposed customer relationship journey to improve profitability and provide actionable insights for broader business strategy.
The development and deployment of predictive models and analytical strategies to improve decision-making throughout the customer life cycle of the TFG account base, from acquisition and new account decisions to existing customer management.
Data mining of credit bureau and other external data to provide insights and trends.
The development of variables/characteristics from external data to improve credit management decisions and the accuracy of predictive models.
Forecasting models/time series analysis to examine portfolio trends and provide strategic tools for business projections.
Model tracking to ensure effective model life cycle management and recommendations for redevelopment.
Effective communication and presentation of analytical results to different stakeholders.
Conducting peer-on-peer quality assurance to ensure consistency in delivery.
Documentation of analytical processes and results, adhering to agreed documentation standards.
To take up this challenging position you should have:
- A degree in a numerate discipline, preferably Statistics / Mathematics / Operations Research (Honours / Masters degree preferable)
- Experience in predictive modelling or scorecard development and of solving complex business, data, and technology problems through leveraging Data and Analytics solution options.
- Experience and knowledge of data engineering tools in AWS and/or Azure as well as analytics tools such as MYSQL, SAS, SAS Enterprise Miner, R, Python, SPSS, MATLAB and BI tools such as QlikView, Power BI, and Tableau is required for technical domains, while experience in MDM, metadata, and data quality toolsets or platforms would be advantageous.
- Knowledge of the credit industry and credit life cycle management.
- Hands-on experience in large-scale customer database data interrogation and manipulation. Experience with data mining and statistical techniques such as logistic regression, decision trees, cluster analysis etc. Excellent data interpretation skills.
- A customer-centric approach. Good strategic and conceptual abilities. Advanced problem-solving, judgement and self-management skills. Design and develop corporate performance management assets like KPIs, scorecards and dashboards.
Preference will be given, but not limited to candidates from designated groups in terms of the Employment Equity Act.
