Join the Credit Optimization team at TFG Financial Services and leverage your expertise in predictive and prescriptive analytics to develop robust models that drive credit risk decision-making.
A challenging opportunity exists within the Credit Optimisation department at TFG Financial Services. We are seeking a dynamic and highly motivated individual to develop analytical solutions to business problems using a range of analytical techniques and tools. The successful candidate will join a team of data scientists focused on creating predictive and prescriptive (mathematical optimization) models to guide credit risk decision-making across the organization.
The role involves developing predictive models that enable mathematical optimization to find optimal solutions within business constraints. You will assist in developing and maintaining mathematical optimization solutions to support crucial decision-making in credit operations. This will require ensuring appropriate statistical methodology and data mining/analytical techniques are employed in the modeling process, delivering robust and effective models. You will extract data accurately and timely for modeling and optimization, developing and maintaining Analytics Based Tables (Credit ABTs) to enhance the accuracy of predictive models. Support model and strategy implementation, testing, and monitoring, and compile documentation of analytical processes and results while adhering to established documentation standards.To qualify for this position, you should have 3+ years of experience in an analytical/data scientist role, specializing in predictive and prescriptive analytics. An Honours degree, or preferably a Master's degree, in mathematics and/or Statistics, including coursework specifically on mathematical optimization (linear programming/mathematical programming), is highly advantageous. Experience utilizing data analysis software packages (SQL, SAS, R, Python, FICO Analytics Workbench), including intermediate to advanced coding skills in one or more of these languages, is essential.Demonstrated experience in formulating mathematical optimization problems (e.g., SAS Proc Opt model) and with data mining and machine learning techniques such as optimization, logistic regression, linear regression, SVM, decision trees, K-means, and cluster analysis is required. Excellent strategic and conceptual abilities, along with strong documentation and verbal communication skills, are crucial. Preference will be given, but not limited to, candidates from designated groups as per the Employment Equity Act
DATA SCIENCE MATHEMATICAL OPTIMIZATION PREDICTIVE MODELING CREDIT RISK ANALYTICAL TECHNIQUES
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