I am working with a global financial institution looking for a Quantitative Risk Modeler who will be responsible for building, implementing, and executing decision models in the consumer portfolio along with providing ad-hoc analytics to support business decisions such as portfolio acquisitions and sales. Candidates should have 3+ years of experience building credit risk models for credit cards and unsecured lending along with proficiency in Python/Hive/SAS/SQL.
- Develop predictive models, statistical analyses, optimization procedures, monitoring processes, data quality analyses, and score implementations supporting regulatory and impairment model
- Shepherd models through the internal validation process
- Implement models in python based Barclays production environments
- Participate in the overall project design and delivery with other functional teams and end-clients
- Produce robust documentation to ensure replicability of results and fulfil governance requirements
- Provide support for audits of model development and implementation, by both internal and external auditors
- Provide other ad hoc analytics as required to support business needs and strategic direction
- Post graduate university degree in quantitative discipline required (ex. Statistics, Operations Research, Economics, Computer Science, Engineering).
- Knowledge of data analysis, theory and statistical techniques (such as linear or nonlinear models, logistic regression, machine learning, macroeconomic forecast, decision trees, cluster analysis and neural networks)
- Experience building loss forecasting and stress testing models for credit cards and unsecured lending
- Python, Hive, SAS and SQL programming skills
- An ability to produce reports and interrogate systems to produce analysis and resolve discrepancies/queries. Analytical, technical and/or statistical skills with proven ability to process large datasets into meaningful information