Quantitative Advisor

Société Générale (SG) is multinational banking and financial services company providing services worldwide in 67 countries. Founded in 1864 and headquartered in Paris, SG is France’s third largest bank by total assets. As a global bank, SG core businesses cover Retail Banking in France (Société Générale), International Banking and Financial Services (IBFS), and Corporate and Investment Banking (SG CIB). SG group reported 2017 operating income of € 4.76 billion, total assets € 1.27 trillion. Team spirit, Innovation, Responsibility, and Commitment – the values upon which SG was founded – have guided the company’s actions for over 150 years.

Model Risk Management (MRM) team embedded within the Risk Management function in SG CIB is responsible for independent validation of models used in SG America regions (US, Canada and Latin America). The team consists of a diversity of talents led by the head of MRM who is active in both industry and academic forums. Training, entrepreneurial spirit, and professional development are encouraged in this growing, dynamic team. With the expansion of model validation tasks, New York based MRM team expands and opens a new office in Montreal Canada.

Role and responsibilities                   

The Quantitative Advisor will execute independent review of business models under both US and Canada regulations working closely with cross functional teams, including business stakeholders, model developer, model validators (Paris and NY office), IT, auditors. Presentation of validation analysis to senior management is in the scope of this role. She/he will be exposed to a variety of models used by the business and support functions, including models for credit risk, market risk, counterparty risk, stress testing, margining, IFRS9, trading algorithms and financial crime compliance.

In collaboration with the Team Manager, the Quantitative Advisor will:

• Conduct independent model review of relevant models that are employed in SG Americas at all stages of their lifecycle by:
  • Assessing model conceptual soundness to ensure the consistency of model design by performing quantitative analyses and statistical tests, developing challenger models for benchmark, reviewing model development processes, and challenging the theoretical aspects considering published research and industry practice;
  • Working with large, complex datasets to verify data input quality and processing (feeding, transformation), model output accuracy, and employ advanced statistical techniques to work with sparse datasets;
  • Assessing data quality and consistency between data characteristics and modeling assumptions;
  • Replicating and review model architecture to verify the computation accuracy of a model and ensure the model is implemented as designed and all model components are functioning as intended
  • Analyzing model output through backtesting, benchmarking, sensitivity analysis by using quantitative tools and techniques;
  • Assessing the model use to ensure it is aligned with the intended purpose by identifying and reviewing model output production, usage, reporting, and business processes;
  • Reviewing model ongoing monitoring to ensure that model is performing as intended by evaluating whether changes in products, exposures, activities, clients, or market conditions necessitate adjustment, redevelopment, or replacement of the model. Verify the model performance over time and ensure that model limitations are assessed;
  • Conducting and interpreting 2LOD model monitoring;
  • Assessing model governance aspects such as model change management, ongoing monitoring, and model risk assessment;
• Evaluate overall model risk, report findings and propose recommendations of remediation. Draft comprehensive validation documents and prepare model review materials for MRM management and committees, RISQ management, and model and business partners; 
• Maintain positive relationships and continuous communication with model and business stakeholders;
• Candidate must be able to communicate model review outcomes (and intermediate feedback) verbally – not only in validation report (mentioned above);
• Work with front office, model developers and risk managers with day-to-day model review and remediation follow-up.