Associate Data Scientist

The Walt Disney Company • New York, NY

1d

About The Position

Would you like to be part of the interdisciplinary team providing vital insights into the rapidly evolving media landscape for Disney’s suite of television networks (ABC, ESPN, etc.) and direct-to-consumer (DTC) streaming services? Join our Data & Analytics Operations team, whose mission is to develop, support, and improve analytics operations by delivering data-driven applications that drive efficiencies across the Research, Insights & Analytics group in Disney Entertainment (DE). We highly value statistical modeling and machine learning approaches—including cross-media measurement and activation across linear, streaming, and digital platforms. We balance advanced methods with pragmatic, classical ones that focus on strong business context, merging the science with the craft. Additionally, we incorporate and deploy modern GenAI-assisted workflows to accelerate analysis and documentation within established privacy and governance standards. Job Summary: As a Data Scientist on the Disney Entertainment (DE) Research, Insights & Analytics team, you’ll develop and improve predictive models that inform advertising and audience dynamics across Disney’s television networks and DTC streaming services (Disney+, Hulu, ESPN+). Primary focus will be viewership and impressions forecasting—built in partnership with Linear and Digital forecasting teams—to optimize Ad Sales yield and support the DTC and Linear P&Ls. You’ll extend this work into cross-media measurement and activation as a whole, delivering clear communications of model accuracy, results, and business impact. You’ll contribute to production-level data-science pipelines and monitoring of models (clean, well-tested Python/SQL; versioning, and QA) and help enhance analysis of complex data for partners in Research, Content, Ad Sales, and Forecasting. You’ll responsibly leverage and deploy AI workflows to improve quality, speed, and depth of analysis—validating outputs before use.

Requirements

• Proficiency in SQL and Python (e.g., pandas, numpy, scikit-learn) for data analysis.
• Experience designing and implementing predictive models (e.g., regression, time series, decision trees, XGBoost, clustering).
• Experience working with Git and collaborative development practices.
• Experience with data visualization tools and applications (e.g. Tableau, plotly, Streamlit)
• Ability to communicate clearly with technical and non-technical audiences.
• User/Client orientation; strong interpersonal skills that build trust across teams.
• Comfort with messy data and flexibility in dynamic environments.
• Experience responsibly using AI-assisted workflows with validation.

Nice To Haves

• Experience with ETL, data pipeline management, and cloud infrastructure for managing large amounts of data (e.g. AWS, PySpark, Snowflake, Airflow, Databricks); Experience using web frameworks (Django) and JavaScript libraries (ReactJS, JQuery) to build professional frontend and backend web applications focused on user experience; Experience building internal tools that help teams operationalize analytics (e.g., small Streamlit/Gradio utilities, scheduled batch jobs). Exposure to managed LLM/AI services inside analytics platforms is a plus.

Responsibilities

• Machine Learning and Statistical Modeling: Design and develop predictive and generative model pipelines by leveraging classical data science and modern AI/ML to impact and measure KPIs; analyze drivers of change and implement improvements that enhance accuracy and stability of cross-media measurement and advance analytics.
• Data Quality: Ensure data is clean and trustworthy; investigate and escalate anomalies; perform robust feature engineering and data prep across linear and streaming sources; contribute to source-of-truth definitions.
• Applications and Communications: Build visualizations and applications to share results with business stakeholders in an easily digestible, sustainable, and automated manner; Analyze data to identify patterns and uncover opportunities.
• Collaboration: Partner closely with peers and business stakeholders to identify and unlock opportunities. Collaborate with other data teams to improve capabilities around data modeling, data platforms, and data visualizations.
• Process Improvement: Drive innovation by exploring new statistical techniques and brainstorming ways to optimize existing infrastructure and make processes even better.
• Operations: Apply Agile principles via participating in standups, sprint planning, writing business requirements documents, and retrospectives; Participate in an “Open Source” learning environment where sharing, documenting, teaching, and collaborating with others is the culture.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 5,001-10,000 employees