Executive Director, AI/ML — Forecasting, Constraint Optimization, and Recommendation Systems

As an AI/ML Executive Director in our AI for Operations organization, you will lead the creation and operation of a complex, constraint-based optimization and recommendation ecosystem supporting the bank’s operations. You will integrate time-series predictions and demand forecasts with optimization models that pursue global, system-wide business objectives while satisfying interdependent operational constraints. This role combines significant business stakeholder management and communication across executive, product, business, and analytics partners with hands-on technical leadership. You will own the solution holistically—from business problem formulation and architecture design through data system creation, AI model development, optimization, deployment, and ongoing production operations.

Job Responsibilities

- Lead the design and delivery of a complex constraint-optimization recommendation system that translates time-series predictions and demand forecasts into actionable operational decisioning

- Formulate global, system-wide optimization objectives, decision variables, and hard and soft constraints with business partners; address competing goals, resource and capacity limits, cross-functional dependencies, and forecast uncertainty to deliver feasible recommendations.

- Develop and validate time-series and demand-driven predictive models, accounting for seasonality, changing demand patterns, and uncertainty; integrate forecast outputs into optimization workflows and evaluate both predictive accuracy and downstream decision quality.

- Build and lead a multidisciplinary team of applied AI/ML researchers, optimization specialists, and engineers; provide technical and career guidance while maintaining hands-on accountability for solution quality and execution.

- Drive significant business stakeholder management across executive leadership and product, business, analytics, data, and engineering partners; translate business needs into technical requirements, align priorities and success measures, and clearly communicate recommendations, trade-offs, risks, and progress to technical and nontechnical audiences. Collaborate with cross-functional partners including product, data and engineering.

- Own production operation of the prediction and optimization ecosystem, including model and data monitoring, forecast drift detection, constraint-feasibility checks, retraining, incident resolution, and continuous improvement of reliability and business outcomes.to

- Drive the full product and technical lifecycle, from problem definition, roadmap, and measurable deliverables through experimentation, back testing, scenario analysis, deployment, adoption, and ongoing performance evaluation.

- Serve as the hands-on technical architect and code contributor for the end-to-end ecosystem; design and build scalable data systems, ingestion and transformation pipelines, forecasting services, optimization engines, recommendation interfaces, and production orchestration with appropriate data quality, governance, and observability.

- Contribute to the continuous learning mindset of the organization by bringing in new knowledge, ideas, and perspectives.

Required qualifications, capabilities, and skills:

- PhD in Computer Science in Artificial Intelligence, Operations Research, or a closely related quantitative discipline, with rigorous education in constraint optimization and core AI/ML methods, a strong computer science foundation, and substantial research and industry experience developing and executing complex AI systems and models.

- Outstanding business stakeholder management and written and oral communication skills, with demonstrated ability to influence executive leadership and partners at multiple levels across product, business, and analytics; explain complex technical concepts, negotiate priorities and constraints, and build alignment around measurable outcomes.

- Extensive hands-on experience developing, deploying, and operating large-scale AI/ML systems, including time-series forecasting, demand prediction, and recommendation or decision systems; demonstrated ownership from model development and validation through production execution and ongoing monitoring.

- Deep expertise in mathematical and constraint optimization, including formulating objective functions and interdependent constraints, selecting appropriate solvers and algorithms, and managing feasibility, scalability, and solution-quality trade-offs across global business goals.

- Advanced, demonstrable programming skills developed over 10+ years of combined doctoral research and industry experience, with proficiency in more than one programming language.

- Strong computer science background in data structures, algorithms, operating systems, compilers, distributed computing, and databases, with demonstrated ability to architect and create scalable data systems and integrate predictive models, optimization services, and production workflows into a reliable technical ecosystem.

Preferred qualifications, capabilities, and skills:

- Advanced research or specialized doctoral work at the intersection of AI/ML, time-series forecasting, demand modeling, and constraint optimization, including mixed-integer, stochastic, robust, or multi-objective optimization.

- Deep experience with distributed infrastructure, data platforms, and computational methods, including distributed ML and scalable optimization, together with production model lifecycle management and delivery of complex forecasting and decision systems in operational environments.

- Preferred technologies include Python, Spark, Scala, and Java, alongside practical experience with AI/ML frameworks and optimization solvers.