About The Role & Team
At Disney Consumer Products
, we inspire imagination around the world
and are committed to creating happiness for families and fans by bringing captivating, inspiring, and inclusive products into their daily lives. From toys to t-shirts, console games, books, and more, our team brings our beloved brands and franchises into the lives of families through products and experiences that can be found worldwide, such as the Disney Store e-commerce platform, Disney Parks, local and international retailers, and Disney Store locations around the world.
The Merchandise Business Insight and Analytics (MBIA) team is part of Disney’s Consumer Products organization, which creates and sells Disney products worldwide. The MBIA team supports the full retail lifecycle by providing analytics, reporting, and insights that empower data-informed decision-making for a broad community of stakeholders across Category and Location Planning, Product Development, Merchandising, and Retail Operations.
We are seeking a
Sr. Data Scientist
to support and drive advanced analytics and data science initiatives for brick-and-mortar and ecommerce retail across Disney Experiences. This role will focus on developing and enhancing machine learning models and data-driven solutions to optimize key areas such as customer segmentation, assortment rationalization, promotion optimization, and performance forecasting. The ideal candidate will have deep expertise in statistical modeling, machine learning, and retail strategy. This position plays a critical role in enabling data-informed decisions that enhance guest experience, drive revenue growth, and support strategic business objectives across digital and physical retail environments.
This role will report to the Sr. Manager, Data Science.
What You Will Do
- Lead End-to-End Model Development: Design, develop, and deploy advanced statistical and machine learning models (e.g., regression, classification, clustering, time series forecasting) to solve complex retail problems including customer segmentation, promotion optimization, and demand forecasting.
- Drive Scalable Data Solutions: Architect and implement scalable data pipelines and model workflows using Python, SQL, and cloud platforms (e.g., Google Cloud Platform, Snowflake) to support continuous data integration and model retraining across large-scale retail datasets.
- Develop Visual Analytics Tools: Build dynamic dashboards and self-service analytics solutions using Tableau, Looker, or similar platforms to monitor key business metrics, track model performance, and deliver actionable insights to cross-functional partners.
- Partner with Business & Technical Stakeholders: Collaborate with product, merchandising, marketing, and technology teams to translate ambiguous business questions into structured analytics problems and deliver insights that drive strategic decisions and operational improvements.
- Mentor and Lead Data Science Best Practices: Guide data scientists and analysts by promoting code quality, model validation, reproducibility, and responsible AI practices including performance monitoring, bias mitigation, and stakeholder education.
Required Qualifications And Skills
- 5+ years as a Data Scientist with subject matter expertise in retail, with experience in areas including customer segmentation, performance forecasting, and business optimization.
- Business Acumen: Ability to translate complex analytical insights into actionable strategies; proven track record of partnering with cross-functional teams such as Merchandising, Operations, Finance, and Marketing to drive revenue outcomes.
- Retail Expertise: Proven experience developing and optimizing retail strategies through advanced analytics, including customer segmentation, demand forecasting, promotional analysis, and performance measurement across both brick-and-mortar channels and ecommerce channels.
- Data Visualization & Dashboarding: Proficient in developing intuitive, impactful dashboards and visual analytics using tools (e.g., Tableau) to enable self-service insights, monitor key retail metrics, and support data-driven decision-making across stakeholders.
- Advanced Statistical Modeling: Deep knowledge of predictive modeling techniques such as regression, time series forecasting (e.g., ARIMA, Prophet), causal inference, and machine learning methods (e.g., XGBoost, LightGBM).
- Experimental Design: Proficiency in designing and analyzing experiments, including A/B testing, geo-lift studies, and other causal frameworks to measure the impact of retail changes.
- Python Proficiency: Expert-level skills in Python, with demonstrated ability to build scalable, production-ready pricing and forecasting models using libraries such as pandas, scikit-learn, statsmodels, and PyMC.
- SQL Expertise: Expert-level skills in SQL for querying and manipulating large-scale retail and transactional datasets, with experience building automated data pipelines.
- Big Data & Cloud Proficiency: Hands-on experience working with large-scale data environments using platforms such as Google Cloud Platform (GCP), Snowflake, BigQuery, and Spark to ingest, process, and analyze complex retail datasets efficiently and at scale.
Education
- Bachelor’s degree in Mathematics, Economics, Data Science, Computer Science, Operations Research, or a related field of study, and/or equivalent work experience.
Preferred Education
Additional Information
- This role is located in Orlando, FL
- As part of the interview process, there will be a skill assessment