Desjardins Group uses a large number of data-driven quantitative models to support its product and service offer and to properly manage its risks, with the goal of always doing what's best for members and clients. While these models provide indisputable decision-making support, their use leads to further risks that must in turn be analyzed, assessed and mitigated. As a data scientist, model validation, you help ensure that data is appropriately valued at each stage of the model's life cycle. You help make business decisions, develop solutions and services tailored to business issues and interpret member/client behaviour. In this respect, you check that the validation programs in place are properly applied to ensure that the models comply with all quality criteria used by Desjardins Group. You make recommendations on the development and use of models with a high degree of operational and conceptual complexity, using your analytical skills and comprehensive, in-depth understanding of your line of business and the organization. You serve as a specialist advisor and subject matter specialist and assist the expert advisor in their role as a resource person for senior management and decision-making bodies. You interact with many interested parties who work in a wide range of fields and in model governance. More specifically, you will be required to:
Ensure that modelling processes make appropriate use of available data based on characterization, quality and processing as part of strategic initiatives
Ensure that predictive modelling is carried out according to the required standards and that Desjardins Group's quality criteria are met through the validation process
Analyze the business needs behind the model and confirm the model's suitability through validation
Ensure that the programming required to prepare and mine data, and to develop, evaluate, support, roll out and manage highly conceptually complex and innovative models meets the required standards
Use advanced and innovative statistical, machine learning and artificial intelligence methods to test the methodologies proposed by the first line of defence
Help draft guidelines and methods and shape methodological and technological choices
Advise and train teams on quantitative model validation methods and update them to improve delivery speed
Identify opportunities to optimize rules and systems, anticipate underlying impacts of changes and help develop data management and modelling support tools
Represent teams with various internal interested parties
Monitor the industry to understand and anticipate trends in your field of expertise in model validation, with a view to developing and updating practices that are contextually appropriate for the organization.
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