Lead Machine Learning Engineer

Job Posting Title:

Lead Machine Learning Engineer

Req ID:

10117825

Job Description:

Disney Entertainment and ESPN Product & Technology

Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally.

The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world. 

Here are a few reasons why we think you’d love working here:

• Building the future of Disney’s media: Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.

• Reach, Scale & Impact: More than ever, Disney’s technology and products serve as a signature doorway for fans' connections with the company’s brands and stories. Disney+. Hulu. ESPN. ABC. ABC News…and many more. These products and brands – and the unmatched stories, storytellers, and events they carry – matter to millions of people globally. 

• Innovation: We develop and implement groundbreaking products and techniques that shape industry norms, and solve complex and distinctive technical problems.

Product Engineering is a unified team responsible for the engineering of Disney Entertainment & ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.

Disney Entertainment and ESPN Product & Technology team is looking for a passionate Lead Machine Learning Engineer to drive the security and operation anomaly detection initiatives within the Commerce, Data, and Identity Alliance.

We're seeking experienced ML engineers to lead on developing and deploying machine learning models that help reduce the security and operation risk for our services. In this role, you’ll work with teams across commerce, growth, and identity to leverage machine learning solutions that enhance the security posture of our services.  

This team is based out of the New York and the Santa Monica offices, and you will be a key leader helping drive the ML initiatives across the team. You will have an opportunity to learn about the various security challenges of a streaming organization. 

Responsibilities

• Design, build, and optimize machine learning models that improve our security posture. 

• Manage the full lifecycle of ML development, including data collection, feature engineering, model selection, evaluation, and production. 

• Collaborate with engineers to deploy models at scale, ensuring robust A/B testing frameworks to assess the impact on key business metrics. 

• Lead exploratory analyses and complex statistical modeling to deliver actionable insights that inform high-stakes business decisions.

• Translate complex data into clear and actionable insights through visualizations, reports, and presentations tailored to both technical and non-technical stakeholders.

• Drive data-informed decision-making by presenting findings that highlight security implications.

• Collaborate with other data teams to improve our data infrastructure, including models, visualizations, and experimentation capabilities. 

• Mentor and grow a high-performing team of data scientists, creating an environment that encourages learning and innovation.

Minimum Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. Master’s degree or PhD is a plus.

• 7+ years of experience in building, deploying, and evaluating real-world machine learning solutions.  

• Proficiency in Python, R, SQL, and other relevant programming languages.

• Experience with data wrangling tools (e.g., Pandas, NumPy), databases (e.g., MySQL, PostgreSQL), and big data platforms (e.g., Spark, Databricks, Jupyter, Snowflake, Airflow, GitHub).

• Expertise in ML libraries such as scikit-learn, SciPy, and related technologies. 

• Strong foundation in statistics, probability, and data mode