Société Générale Corporate & Investment Banking (SGCIB) is the third largest investment bank in the European Economic Area and is present in more than 75 countries around the world.
The head office of its Canadian securities broker is located in Montreal and has approximately 100 employees. Société Générale Group's SGCIB division has positioned itself as a front-
line player in the trading of interest rate and currency derivatives on the Canadian market.
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
The 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. He / she 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 Senior Quantitative Advisors and 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 back testing, 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.
Requirements for the Quantitative Advisor :
Minimum of a Bachelors Degree (Mater and PhD preferred) in a quantitative area : Mathematical Finance, Financial
Engineering, Statistics, Economics, Computer Science, Technology, Engineering and Mathematics;
1-3 years of experience in Model Development or Validation in finance / risk management, or Front Office Quant role.
Fewer years of relevant experience will be considered for candidates with a PhD degree;
Excellent quantitative programming skills in at least one programming language (e.g. Python, R, C++, SAS, Matlab);
Advanced knowledge of statistics, econometrics, machine learning;
Strong verbal and written communication skills with the ability to work with quant or non-quant staffs;
Awareness of model risk management and associated regulatory requirements;
Team-oriented with a keen sense of ownership and accountability;
Project and time management skills to work in a multi-tasking working environment;
Experience with various quantitative models in areas of Market Risk, Credit Risk, Operational Risk and PPNR is a plus;
Experience in large data management and quantitative analysis is a plus;
Bilingual (English and French) is a plus;
FRM or other Risk Management certifications is a plus;
PhD is a plus.