Job
Description
You will be responsible for overseeing business activities, models, and methodologies related to investment risk with a focus on developing new tools and methodologies for risk quantification. Your key responsibilities will include: - Designing, developing, and deploying advanced AI/ML models to derive actionable insights, automate processes, and facilitate strategic decision-making across various business units. - Collaborating with teams to design and oversee the end-to-end lifecycle of AI/ML initiatives, from problem scoping, data engineering, model development, validation, deployment, to monitoring. - Leading the model development process, including tasks such as data wrangling/analysis, model training, testing, and selection. - Conducting risk modeling, scenario analysis, and business impact evaluations to ensure AI/ML solutions are robust, ethical, and aligned with organizational goals. - Driving experimentation with advanced techniques such as deep learning, generative AI (e.g., LLMs, GANs), and reinforcement learning to solve complex business challenges and create value. - Utilizing advanced statistics, econometrics, and mathematical skills including graph theory applications, probability theory, stochastic calculus, Monte Carlo simulation, numerical analysis, optimization techniques, and time series analysis. - Collaborating with Technology on model testing, implementation, and production. - Providing mentorship and oversight for junior data scientists to build a collaborative working culture. - Delivering written, visual, and oral presentations of modeling results to business and technical stakeholders. - Staying abreast of emerging trends and research in AI and machine learning, and assessing their potential application within the organization. - Contributing to continuous innovation and improvement in the efficiency and effectiveness of processes within the asset-class group, including discussions on overall team strategy. - Representing risk analytics in governance forums, risk committees, and audit discussions. - Working with risk managers and other stakeholders to address requests for additional analysis based on specific needs. - Participating in regulatory and validation exams by providing documentation and responses to regulators and internal validators. - Partnering with non-credit risk groups to identify and understand the simultaneous impact of multiple risks, such as product, fiduciary, counterparty, concentration, ESG/climate, liquidity, and their effect on investments. Required qualifications, capabilities, and skills: - 5+ years of hands-on experience in data science, machine learning, or AI. - Bachelors/Master/PhD degree in Computer Science / Data Science / Mathematics / Statistics / relevant STEM field is highly preferred. - Demonstrated experience with generative AI technologies such as transformers, large language models, or diffusion models. - Knowledge of key concepts in Statistics and Mathematics such as Statistical methods for Machine learning (e.g., ensemble methods, NLP, time-series), Probability Theory and Linear Algebra. - Experience with Machine Learning & Deep Learning concepts including data representations, neural network architectures, custom loss functions. - Foundational mathematical concepts, including continuity, differentiability, Taylor formulas, differential equations, integration, measure theory, linear algebra, discrete and continuous probabilities, Markov chains, regression. - Experience with investment products including fixed income, equity, and mutual funds. - Programming skills in Python and knowledge of common numerical and machine-learning packages (like NumPy, scikit-learn, pandas, PyTorch, LangChain, LangGraph etc.). - Experience with data visualization tools such as Tableau, Power BI, or similar. - Logical thought process, ability to scope out an open-ended problem into a data-driven solution. - Strong quantitative and analytical skills and ability to work with diverse cultures in a global team.,
