Director, Decision Science AI/ML Engineering & Ops

The Walt Disney Company • Burbank, CA

6d • Hybrid

About The Position

The Disney Decision Science and Integration (DDSI) team is seeking a visionary leader to bridge the gap between world-class decision science and industrial-scale engineering. This role will be the architect of the "Science Factory," ensuring ensemble models and custom algorithms are scalable, observable, and resilient. The Director will lead the core function that productionizes decision science within DDSI for efficient and effective deployment into SaaS products. This foundational leadership role is responsible for building the technical backbone to support next-generation, AI-powered products. The Director will form and mentor a specialized team of AI/ML engineers to create a robust, automated, and scalable factory for deploying the portfolio of ensembled science models and custom algorithms. AI/MLOps will be treated as a product, providing Disney’s decision scientists with the necessary building blocks, feature stores, and automated pipelines to innovate at scale. The mission is to increase the speed-to-market and reusability of integrated algorithms that turn data into recommendations via models developed and coded by scientists. The team will create advanced tools to empower scientists and expert modelers with configurable building-blocks, automated capabilities, automated testing & monitoring, and streamlined AI/MLOps processes, fostering an AI-powered engineering culture to accelerate innovation and push the envelope on both speed-to-market and model sophistication & consumability. The goal is to eliminate the friction between model development and deployment. The role will involve working on greenfield AI initiatives and maintaining complex production systems.

Requirements

• 12+ years of related experience
• Prior experience leading decision scientists and/or machine learning engineers to deploy production solutions
• Sufficient statistical and modeling fluency to partner effectively with decision scientists — including the ability to reason about model behavior, diagnose drift or degradation, and assess output integrity in production environments
• Experience with analytical coding languages such as Python, R, SQL
• Experience designing and implementing complex algorithms within constraints for performance, time-to-market, and adoptability
• Experience with a breadth of mathematical modeling approaches, including but not limited to supervised learning, unsupervised learning, reinforcement learning, forecasting, estimation, optimization and/or simulation techniques
• Ability to learn technical methods and tools independently
• Strength in leadership to navigate complex organizational dynamics, remove barriers, and be a thought partner for all levels
• Experience with software development tools (e.g. GitLab/GitHub, Docker, CI/CD practices, etc.)
• Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study and/or equivalent work experience

Nice To Haves

• Experience with genAI capability development (e.g., not just AI to develop, but developing AI)
• Cloud computing concepts including auto-scaling, AWS infrastructure & services
• Familiarity with emergent design patterns including agent-driven solutions, interactive LLM/genAI implementations, and beyond
• Master’s degree in Computer Science, Computer Engineering, or related discipline, or MBA

Responsibilities

• Develop and maintain a team vision in a fast-paced, complex, and evolving arena.
• Foster a high-performing team of AI/ML engineers and drive a culture of excellence, innovation, and deep collaboration with the science organization and partner teams.
• Define and execute a comprehensive MLOps roadmap.
• Architect and implement repeatable and common practices across the project portfolio, including automated model sustainment & monitoring, interoperable and configurable science packages/agents, feature stores, and governance.
• Manage a high-performing team in a matrixed environment, acting as the technical translator between Science development teams and the DS Technology organization.
• Define and evolve the AI/ML engineering skill mix, career paths, and hiring strategy.
• Design, build, and champion a library of configurable and reusable building blocks (e.g., feature engineering modules, model templates) for scientists and modelers.
• Develop roadmaps for reusable capabilities, tools, and agents to harmonize with portfolio milestones and deliverables.
• Co-design and engineer scalable batch and/or callable science services for ensembled models and custom algorithms.
• Champion the adoption of a portfolio-wide metrics process to increase visibility of KPIs (e.g., batch performance, data quality, model reliability).
• Implement rigorous automated testing, validation suites for algorithmic guardrails, and KPI dashboards.
• Proactively identify and remediate technical debt within ML pipelines, balancing new feature velocity with core stability.
• Collaborate with decision scientists in rapid response to batch process failures and service outages.
• Drive culture and build systems to identify failure causes, implement permanent fixes, and oversee technical recovery of production environments.
• Ensure capabilities for model output explainability are embedded by design.
• Foster a culture of innovation by leading the adoption of AI tools within the development process (e.g., code assistants, automated testing).
• Support scientists and product teams with process & tool adoption via documentation and training for reusable building blocks.
• Serve as the primary partner for the Decision Science Delivery team on all aspects of model & algorithm productization.
• Collaborate closely with Directors of Decision Science Technology to ensure seamless integration and deployment of AI/ML services.
• Establish intake and prioritization mechanisms that maximize reuse, standardization, and enterprise value across the decision science portfolio.
• Connect business partners, clients, and the team with process improvements and the adoption of the latest business, science, and technology standards and best practices.
• Ensure all AI/ML platforms and services are designed with security, privacy, explainability, and Responsible AI principles embedded by default.
• Partner with appropriate teams to ensure compliance with enterprise and regulatory standards.
• Ensure cost-aware design of AI/ML capabilities, balancing experimentation velocity with sustainable cloud and compute economics.
• Partner with teams to ensure responsible scaling of AI/ML/science workloads.
• Operate at all levels of the organization, including tactical project leadership, strategic planning, and business-focused consulting.

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package
• Full range of medical, financial, and/or other benefits, dependent on the level and position offered

What This Job Offers

• Job Type: Full-time
• Career Level: Director