Investment Risk

Role Overview: You will be part of the Wealth Management (WM) Investment Risk & Analytics (IR&A) team, responsible for overseeing the risk of Managed Strategies across the WMs Private Bank and Consumer Bank businesses. Your main focus will be on partnering with senior team members to develop new tools and methodologies for investment risk quantification. Key Responsibilities: - Design, develop, and implement advanced AI/ML models to automate processes and provide actionable insights for strategic decision-making in various business units. - Collaborate with teams to manage the entire lifecycle of AI/ML initiatives, from problem scoping to model deployment and monitoring. - Lead the model development process, including data analysis, model training, testing, and selection. - Conduct risk modeling, scenario analysis, and business impact evaluations to ensure robust and ethical AI/ML solutions aligned with organizational goals. - Experiment with advanced techniques like deep learning, generative AI, and reinforcement learning to solve complex business challenges. - Work with Technology teams on model testing, implementation, and production. - Present modeling results to business and technical stakeholders through written, visual, and oral communication. - Stay updated on emerging trends and research in AI and machine learning for potential application within the organization. - Represent risk analytics in governance forums, risk committees, and audit discussions. - Participate in regulatory and validation exams by providing necessary documentation and responses. - Partner with non-credit risk groups to understand the impact of multiple risks on investments. Qualifications Required: - 3+ years of hands-on experience in data science, machine learning, or AI. - Bachelor's/Master's/PhD degree in Computer Science, Data Science, Mathematics, Statistics, or a relevant STEM field preferred. - Experience with generative AI technologies like transformers, large language models, or diffusion models. - Knowledge of key concepts in Statistics and Mathematics relevant to Machine Learning, Probability Theory, and Linear Algebra. - Familiarity with investment products such as fixed income, equity, and mutual funds. - Proficiency in Python programming and common numerical and machine-learning packages like NumPy, scikit-learn, pandas, PyTorch, etc. - Experience with data visualization tools such as Tableau, Power BI, or similar. - Strong analytical skills, logical thought process, and ability to translate open-ended problems into data-driven solutions. - Ability to work effectively in a global team with diverse cultures. Role Overview: You will be part of the Wealth Management (WM) Investment Risk & Analytics (IR&A) team, responsible for overseeing the risk of Managed Strategies across the WMs Private Bank and Consumer Bank businesses. Your main focus will be on partnering with senior team members to develop new tools and methodologies for investment risk quantification. Key Responsibilities: - Design, develop, and implement advanced AI/ML models to automate processes and provide actionable insights for strategic decision-making in various business units. - Collaborate with teams to manage the entire lifecycle of AI/ML initiatives, from problem scoping to model deployment and monitoring. - Lead the model development process, including data analysis, model training, testing, and selection. - Conduct risk modeling, scenario analysis, and business impact evaluations to ensure robust and ethical AI/ML solutions aligned with organizational goals. - Experiment with advanced techniques like deep learning, generative AI, and reinforcement learning to solve complex business challenges. - Work with Technology teams on model testing, implementation, and production. - Present modeling results to business and technical stakeholders through written, visual, and oral communication. - Stay updated on emerging trends and research in AI and machine learning for potential application within the organization. - Represent risk analytics in governance forums, risk committees, and audit discussions. - Participate in regulatory and validation exams by providing necessary documentation and responses. - Partner with non-credit risk groups to understand the impact of multiple risks on investments. Qualifications Required: - 3+ years of hands-on experience in data science, machine learning, or AI. - Bachelor's/Master's/PhD degree in Computer Science, Data Science, Mathematics, Statistics, or a relevant STEM field preferred. - Experience with generative AI technologies like transformers, large language models, or diffusion models. - Knowledge of key concepts in Statistics and Mathematics relevant to Machine Learning, Probability Theory, and Linear Algebra. - Familiarity with investment products such as fixed income, equity, and mutual funds. - Proficiency in Python programming and common numerical and machine-learning packages like NumPy, scikit-lea