Senior Manager, AI/ML Engineering - Workforce Management

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. The ideal candidate will be a collaborative leader, committed to developing talent, championing technical rigor, and enabling AI,ML and optimization at scale across the organization.

• 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, containerization, observability, model monitoring, drift detection, and retraining or recalibration processes.
• Deep expertise in statistical inference, supervised and unsupervised learning, regression, classification, clustering and segmentation, time-series forecasting, probabilistic modeling, predictive analytics, and model ensembling.
• Demonstrated expertise formulating and solving large-scale decision problems using linear, mixed-integer, nonlinear, stochastic, robust, and/or multi-objective optimization; experience with constraint programming, decomposition, simulation, heuristics, and learning-based decision methods.
• Advanced proficiency in Python and SQL, with experience using optimization modeling frameworks and one or more solver ecosystems such as Gurobi or CPLEX.
• Experience integrating optimization with machine learning, forecasting, predictive models, personalization and recommendation systems, experimentation, simulation, or adaptive decision methods.
• Proven ability to establish technical vision, influence and direct multidisciplinary teams without formal authority, drive cross-functional alignment, and resolve complex technical and business decisions.
• Track record of connecting technical solutions to tangible business value and managing a portfolio of initiatives across multiple business domains.
• Storyteller comfortable presenting to executives and mentoring both technical and non-technical audiences.
• Hands-on experience applying privacy, security, fairness, explainability, governance, and compliance principles in production AI/ML and optimization systems.
• Advanced knowledge of product and delivery methodologies, with demonstrated success translating strategy into coordinated execution in fast-paced, ambiguous, and matrixed environments.

• Master’s degree in Computer Science, Engineering, Data Science or in a relevant applied quantitative field is required.

• 9+ years’ experience in applied‑AI, data science or software‑engineering roles;
• or equivalent combination of education and experience.

Your talent, skills and experience will be rewarded with a competitive compensation package.

Universal is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Universal Orlando via-email, the Internet or in any form and/or method without a valid written Statement of Work in place for this position from Universal Orlando HR/Recruitment will be deemed the sole property of Universal Orlando. No fee will be paid in the event the candidate is hired by Universal Orlando as a result of the referral or through other means.

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