Do you thrive on transforming brilliant and complex science into robust, scalable software? Are you driven to advance the platforms and tools that empower scientists to do their best work, faster? Are you energized about building the capabilities that allow data scientists to move from "proof-of-concept" to "global production" with the push of a button? We are looking for a visionary leader to bridge the gap between world-class decision science and industrial-scale engineering.

The Disney Decision Science and Integration (DDSI) team is the engine behind science-driven decision-making across The Walt Disney Company . We leverage advanced algorithms and scientific approaches such as optimization, machine learning, simulation, statistical modeling, genAI and beyond (“decision science”) within innovative software as a service (SaaS) products that shape business decisions across The Walt Disney Company. We support client areas including Disney Entertainment (ABC, The Walt Disney Studios, Disney+, Hulu, ESPN), Disney Experiences (Theme Parks, Cruise Line, Consumer Products, DVC), Corporate Finance, and others, with strategic applications that enable science-driven decision-making and drive business value.

Team Description:

As the Director, Decision Science AI/ML Engineering & Ops, you will be the architect of our "Science Factory," ensuring our ensemble models and custom algorithms are scalable, observable, and resilient. You 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 our next-generation, AI-powered products. You 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. You will treat 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. Working hand-in-hand with decision scientists, your mission is to increase the speed-to-market and reusability of the integrated algorithms that turn data into recommendations via models developed and coded by scientists. You and your team will create advanced tools to empower our scientists & expert modelers with configurable building-blocks, automated capabilities, automated testing & monitoring, and streamlined AI/MLOps processes - all while fostering an AI-powered engineering culture to accelerate innovation and push the envelope on both speed-to-market and model sophistication & consumability. In other words, you will lead a specialized team dedicated to leveling-up the speed to market of decision science, and ensuring our scientists are supercharged with repeatable creation via automation and reusable components. Your goal is to eliminate the friction between model development and deployment. The role will not only be working on greenfield AI initiatives but also comprises stewardship towards maintenance of existing complex ecosystem of production systems.

What You’ll Do:

• Team Vision: Develop and keep relevant a vision for 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 all partner teams.
• MLOps Strategy & Capability Oversight: Define and execute a comprehensive MLOps roadmap. Architect and implement repeatable and common practices across portfolio of projects, including but not limited to automated model sustainment & monitoring, highly interoperable and configurable science packages and/or agents, feature stores, and governance required to support complex, ensembled, and algorithm-driven systems.
• Strategic Leadership: Manage a high-performing team in a matrixed environment. You will act as the “technical translator” between the Science development teams and the DS Technology organization to ensure our AI/ML services are interoperable with DDSI’s infrastructure, as it continue to evolve in the context of changing toolsets in an AI environment. Define and evolve the AI/ML engineering skill mix, career paths, and hiring strategy required to support DDSI’s long-term science-to-production vision.
• Reusable Building Blocks Creation: Design, build, and champion a library of highly configurable and reusable building blocks (e.g., feature engineering modules, model templates, etc) for scientist and modelers to use, accelerating their model development cycle and reducing time-to-production.
• Design Pattern Definitions: Develop roadmaps for reusable capabilities, tools, and agents to harmonize with the portfolio milestones & deliverables while simultaneously raising the bar on standard expectations for deployed algorithms, including automated metrics and validation, user-algorithm interactions, and standard features for robust algorithmic guardrails and adaptive-yet-stable solution design.
• Productization & Service Design: Partner directly with Decision Science Delivery team co-design and engineer scalable batch and/or callable science services for ensembled models and custom algorithms.
• Operational Excellence: Champion the adoption of a portfolio-wide metrics process to increase visibility of KPIs including batch performance, data quality, model reliability/decision integrity, etc., enabled by the development and implementation of common tools and reusable packages across the portfolio that automate metric capture. Establish a "Production First" culture. Implement rigorous automated testing, validation suites for algorithmic guardrails, and KPI dashboards that track the health of models in the wild.
• Technical Debt & Modernization: Proactively identify and remediate technical debt within the ML pipelines. You will balance the "velocity of new features" with the "stability of the core," ensuring that our internal SaaS products remain modern, patchable, and secure.
• System Maintenance Stewardship & Operational Reliability: Collaborate with decision scientists in rapid response to batch process failures and service outages, ensuring internal business partners face minimal disruption. Drive culture and build systems to identify why a system failed—whether due to data drift, pipeline bottlenecks, or algorithmic edge cases—implement permanent fixes, and oversee the technical recovery of production environments, balancing the need for speed with the integrity of the underlying science. Ensure capabilities to drive model output explainability embedded by design for all deployed solutions.
• Champion AI-Powered Productivity: Foster a culture of innovation by leading the adoption of AI tools within the development process (e.g., code assistants, automated testing) to enhance team efficiency, code quality, and speed. Ensure AI/MLE & Ops team supports scientists and product teams with process & tool adoption via documentation and training for reusable building blocks.
• Cross-Functional Partnership: Serve as the primary partner for Decision Science Delivery team on all aspects of model & algorithm productization. Collaborate closely with the Directors of Decision Science Technology to ensure seamless integration and deployment of AI/ML services. Partner closely across functional areas to lead directly and via collaboration in a matrixed environment, with emphasis on strong communication, interpersonal collaboration and change management skills
• Demand Management & Portfolio Prioritization: Establish intake and prioritization mechanisms that maximize reuse, standardization, and enterprise value across the decision science portfolio.
• Change Management: Connect business partners, clients and team with processes improvements and the adoption of the latest business, science and technology standards and best practices
• Stewardship: 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.
• Communication Agility & Influence: Ability to operate at all levels of the organization, including tactical project leadership, strategic planning, and business-focused consulting with clients and executives at all levels. Demonstrated interpersonal skills, with ability collaborate effectively with colleagues ranging from entry-level professionals to high-level executives

Required Qualifications & Skills:

• 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.)

Preferred Qualifications:

• 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

Required Education:

• Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or comparable field of study and/or equivalent work experience

Preferred Education:

• Master’s degree in Computer Science, Computer Engineering, or related discipline, or MBA

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The hiring range for this position in Orlando, FL is $217,800 to $292,100 per year and in Burbank, CA is $228,700 to $306,700 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.