Applied AI ML Lead

We have an exciting and rewarding opportunity for you to advance your AI-ML modeling career in our Finance Modeling team. As an AI-ML Lead in the Finance Modeling team, you design and deliver innovative models that support informed decision-making and business growth. You collaborate with diverse teams and contribute to the firm’s success through advanced analytics and model development. Job Responsibilities Perform advanced quantitative and statistical analysis of large datasets to uncover trends and insights Build statistical, econometric, or machine learning models for budgeting, financial analysis, regulatory requirements, and pricing decisions Communicate analytical results to Finance partners, modeling teams, and Model Governance Experience in leading & managing teams to deliver best in class statistical, econometric & ML models Required qualifications, capabilities, and skills Graduate degree (M.S. or Ph.D.) in Statistics, Economics, Mathematics, Operations Research, Engineering, or Computer Science 10+ years of hands-on model development experience Proficient in Python & PySpark with strong programming and development skills Experience with statistical and econometric modeling techniques, including time series, panel data, Bayesian, and non-parametric methods Strong foundation in machine learning theory and end-to-end development, including NLP, computer vision, or reinforcement learning Proficient in big data processing tools such as Spark or Hadoop and Unix operating systems Ability to communicate complex concepts effectively with non-technical stakeholders Experience with machine learning models and familiarity with Gen AI applications Expertise in Python, with knowledge of PySpark or TensorFlow Excellent written and oral communication and presentation skills Experience in leading & managing high performance teams to deliver statistical, econometric, or machine learning models Preferred qualifications, capabilities, and skills Hands-on experience in budget and regulatory (CCAR) modeling for deposit growth, fee revenue, or other business drivers Knowledge of components of PPNR models for deposit & wealth management portfolio Understanding forecasting the performance of branches or bankers, in order to optimize the branch network and staffing Creating price elasticity models to optimize deposit and loan (Auto, Home Lending & Cards) pricing Model-based automatic machine learning Machine Learning Explainability for risk control Scalable Machine Learning / Big data framework