Director, Decision Science AI​/ML Engineering & Ops

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:

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** 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 product ionizes 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:*
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** 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.

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** 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.

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** 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.

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** 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.

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** 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…