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