Director, Decision Science AI/ML Engineering & Ops
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 architect 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 team will focus on treating AI/MLOps as a product, providing 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, eliminate friction between model development and deployment, and foster an AI-powered engineering culture. The role also includes stewardship for the maintenance of existing 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
- 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.
- Define and execute a comprehensive MLOps roadmap, architecting and implementing repeatable practices.
- Manage a high-performing team in a matrixed environment, acting as a technical translator between Science and Technology organizations.
- 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 for scientists and modelers.
- Develop roadmaps for reusable capabilities, tools, and agents to harmonize with portfolio milestones.
- Partner with the Decision Science Delivery team to co-design and engineer scalable science services.
- Champion the adoption of a portfolio-wide metrics process to increase visibility of KPIs.
- Establish a "Production First" culture with rigorous automated testing, validation suites, and KPI dashboards.
- Proactively identify and remediate technical debt within ML pipelines.
- Collaborate with decision scientists in rapid response to batch process failures and service outages.
- 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.
- 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 for seamless integration and deployment.
- 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 standards.
- Ensure all AI/ML platforms and services are designed with security, privacy, explainability, and Responsible AI principles.
- Ensure cost-aware design of AI/ML capabilities, balancing experimentation velocity with sustainable cloud and compute economics.
- 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
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Director
