Director, Decision Science AI/ML Engineering & Ops

The Walt Disney Company • Burbank, CA

3d • $217,800 - $306,700 • Onsite

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 our "Science Factory," ensuring our 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 is a foundational leadership role responsible for building the technical backbone to support next-generation, AI-powered products. The individual will form and mentor a specialized team of AI/ML engineers to create a robust, automated, and scalable factory for deploying our portfolio of ensembled science models and custom algorithms. The focus is on treating AI/MLOps as a product, providing Disney’s decision scientists with the building blocks, feature stores, and automated pipelines they need 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. This includes creating advanced tools for scientists and expert modelers with configurable building-blocks, automated capabilities, automated testing & monitoring, and streamlined AI/MLOps processes, while fostering an AI-powered engineering culture. The goal is to eliminate friction between model development and deployment, encompassing stewardship towards maintenance of existing complex ecosystems of 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.)

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

Responsibilities

• Develop and maintain a vision for the team 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 portfolio of projects, including automated model sustainment & monitoring, highly interoperable and configurable science packages and/or 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 highly configurable and reusable building blocks for scientists and modelers.
• Develop roadmaps for reusable capabilities, tools, and agents to harmonize with portfolio milestones.
• Partner directly with the Decision Science Delivery team to co-design and engineer scalable batch and/or callable science services.
• Champion the adoption of a portfolio-wide metrics process to increase visibility of KPIs.
• Establish a "Production First" culture.
• Implement rigorous automated testing, validation suites for algorithmic guardrails, and KPI dashboards.
• Proactively identify and remediate technical debt within ML pipelines.
• Balance the "velocity of new features" with the "stability of the core."
• Collaborate with decision scientists in rapid response to batch process failures and service outages.
• Drive culture and build systems to identify why a system failed and implement permanent fixes.
• Oversee the technical recovery of production environments.
• Ensure capabilities to drive model output explainability are embedded by design.
• Foster a culture of innovation by leading the adoption of AI tools within the development process.
• Ensure the AI/MLE & Ops team supports scientists and product teams with process & tool adoption via documentation and training.
• Serve as the primary partner for the Decision Science Delivery team on all aspects of model & algorithm productization.
• Collaborate closely with the Directors of Decision Science Technology for seamless integration and deployment of AI/ML services.
• Establish intake and prioritization mechanisms that maximize reuse, standardization, and enterprise value.
• 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.
• Partner with teams to ensure responsible scaling of AI/ML/science workloads.

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package
• the 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