Senior Manager, AI​/ML Engineering

The Senior Manager, AI/ML Engineering will lead a multidisciplinary team of data scientists, AI/ML engineers, data engineers, and developers to design and deploy enterprise-scale optimization and decision-intelligence solutions that drive measurable business outcomes. This role combines strategic vision with hands-on technical leadership, serving as an enterprise subject matter expert for machine learning, optimization and mathematical programming while guiding teams to deliver production-ready systems that enhance business performance and guest experiences.

Key responsibilities include managing a portfolio of applied AI/ML initiatives across personalization, marketing, dynamic pricing, revenue management, workforce planning, digital experiences, and other complex decision domains; translating strategy into execution; embedding responsible AI and decision-system practices; and fostering a culture of innovation.

• Provide strategic and technical leadership to multidisciplinary teams of data scientists, AI/ML engineers, data engineers, and developers, setting direction and aligning work to enterprise priorities while promoting collaboration, continuous learning, and high-impact delivery.
• Establish and communicate a clear enterprise vision, technical roadmap, and operating model for optimization and decision intelligence, balancing immediate business needs with long-term capability development.
• Direct technical teams by defining objectives, decision rights, technical standards, architectural guardrails, and measurable outcomes.
• Guide and mentor technical teams through problem formulation, architecture, model design, solution trade-offs, and production readiness in support of machine learning, optimization and mathematical programming.
• Coordinate initiative sequencing, resource needs, dependencies, and delivery risks across product, engineering, data, and business teams, removing roadblocks and ensuring execution remains aligned with scope, schedule, and business outcomes.

• Provide technical direction and oversight for AI/ML and optimization development, architecture, code quality, reusable libraries, reference architectures, and product-lifecycle best practices to ensure scalable, maintainable, secure, and production-ready solutions.
• Engage with internal and external partners to gather requirements and collaborate with engineers, architects, product leaders, and business stakeholders to design robust solutions across domains.
• Lead end-to-end problem formulation, model design, algorithm selection, prototyping, benchmarking, and validation across optimization, machine learning, and decision science. Apply advanced analytical and AI/ML techniques to integrate predictive and prescriptive modeling, experimentation, simulation, real-time signals, and business constraints into scalable decision systems that improve business outcomes and adapt over time.
• Establish delivery and operational governance, remove cross-team roadblocks, and track quality, reliability, adoption, and business-impact KPIs. Define standards for model and solver monitoring, performance degradation and drift detection, incident response, and continuous improvement.

• Define and drive the strategy and prioritized portfolio for optimization products and decision systems, balancing near-term business outcomes with long-term platform evolution and determining where capabilities should be built, bought, reused, or standardized across personalization, marketing, dynamic pricing, revenue management, workforce planning, digital experiences, and resource allocation.
• Evaluate open-source and commercial solvers, frameworks, and platforms across performance, cost, scalability, licensing, and security; direct cross-functional alignment on technology choices, investments, standards, and responsible-AI requirements, and communicate recommendations, trade-offs, and program status to senior leadership.
• Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
• Performs other duties as assigned.

• Hands-on experience designing and developing production decision systems, including data and feature pipelines, APIs and services, cloud platforms such as AWS, GCP, or Azure, version control, automated testing, CI/CD,…