Modelling/Forecasting Analyst

  • 38246
  • Life - Data Science
  • |
  • Ontario, Canada
  • |
  • Sep 23, 2020
Insurance
RESPONSIBILITIES
  • Responsible for the development, initial validation and documentation of account-level credit risk parameter models for the retail credit product portfolios, as well as supporting all stages of model validation, audit and implementation.
  • Will ensure compliance with the company Model Risk Policy, Capital Model Approval Policy, Data Governance requirements, and with other relevant policies and regulatory requirements.
  • Will work closely with and actively support company Retail Risk Management, as well as retail credit product and finance areas by providing a deep analysis of credit risk drivers and parameters under various scenarios for the respective product portfolios.
  • Will use leading-edge technologies and develop innovative solutions in the following areas:
  • Data mining, making sense of some very large databases of credit risk related historical data.
  • Predictive credit risk modelling based on rigorous statistical analysis of historical data, regression techniques, and econometric analysis.
  • Estimating credit risk imbedded in the company’s retail credit product portfolios, and the amount of regulatory and economic capital the company needs to allocate against these portfolios.
QUALIFICATIONS
  • A university degree in Statistics or in a related quantitative discipline.
  • Strong working knowledge and hands-on experience using SAS and SQL in the context of data manipulation, data mining, statistical analysis, and predictive modelling.
  • Proficiency in creating and manipulating large data sets for data mining and predictive statistical modelling.
  • Working knowledge of concepts and methodologies, such as retail credit risk scoring techniques, used in the assessment of credit risk for retail credit exposures.
  • Strong working knowledge of modern statistical model development and validation concepts and techniques, in particular linear and logistic regression techniques.
  • Strong project management skills and able to prioritize and manage workload to deliver quality results and meet assigned timelines.
  • Support a positive work environment that promotes service to the business, quality and team work and ensure timely and effective communication.
  • Strong problem-solving skills, ability to independently identify and solve problems in an effective and timely manner.
  • Strong communication skills to establish effective relationships across multiple business stakeholders.