Do you love the art of using data to solve complex business problems?   We do too – and would be excited to have you join our team at Walt Disney World!

Our mission as part of the Commercial Strategy organization is to put our data assets to work and apply quantitative techniques to discover previously unknown insights about our Guests and business.   We get to work directly with the major decision-makers at Disney who shape the Walt Disney World experience.  Through our insights, we help form and improve Disney products and offerings.

This is a unique role, in that it looks across a variety of functional areas, and enables the creation of holistic models to better predict and prescribe improvements to the overall Walt Disney World business.   A position like this puts you in the middle of the main commercial decisions of the company – and is a great way to showcase your highly-technical data science and data engineering skills!

The Professional Intern – Data Science currently reports to the Lead Data Science and is an individual contributor role.

Position Responsibilities

While each day will be different, with rotating priorities, the main themes for this role include you being a(n):

• Contributor.  You will use your skills to develop data structures, documentation, and analysis to drive value & efficiency for the team.

• You will work on tools, processes, reporting, and documentation focused on the Commercial data ecosystem.
• You will create data solutions, answer adhoc business questions, and help your team by addressing data concerns or questions.
• You will adhere to best practices, including but not limited to code reviews, automated testing and coverage, version control, CICD, configuration management, and clear documentation as you support large data and analytic projects.

• Collaborator.  You will use your interpersonal skills to develop relationships that lead to successful data solutions.

• You will work with Data Product Managers, Data Architects and Data Engineers to design, implement, and deliver successful data solutions for the Commercial organization.
• You will build partnerships with data scientists and data analysts, leveraging your knowledge of analytics and machine learning concepts, to enable the development of impactful analytical products.

• Learner.  You will lean into your curiosity to understand business problems, data environments, and people!

• You will learn about existing and emerging data engineering and analytical solutions, capabilities, and technologies and be able to support, maintain, and mature them.
• You will learn about different teams, functions, and locations, which will help you to understand the complex operation, as well as learn to work with many different personalities and skill levels as they troubleshoot different technology solutions.

Basic Qualifications

• Experience with SQL, Python, or R to perform data manipulation, engineering, or analysis
• Familiarity with SQL or NoSQL databases, as well as data modeling techniques/standard methodologies and practices
• Knowledge of industry leading data and analytic tools (e.g., Snowflake, Git, Sagemaker, Dataiku, Tableau)
• Ability to follow existing programming and style guidelines to maintain consistent quality of code
• Ability to maintain documentation, procedures, and working instructions for the Commercial team
• Time-management skills, with ability to prioritize multiple requests and communicate status to partners/leadership
• Strong communication, interpersonal skills and fortitude to get stuff done
• Passionate about data, technology, and challenging the status quo

Preferred Qualifications

• Familiarity with Docker and Kubernetes
• Familiarity with Machine Learning
• Experience with using ML Frameworks, libraries, and tools, such as TensorFlow, Spark MLlib, PyTorch, scikit-learn, Keras, etc.
• Experience with cloud platforms, such as AWS, Google Cloud, or Azure, as well as big data technologies

Required Education

• Currently enrolled undergraduate student studying Computer Science, Computer Engineering, Data Engineering, Information Systems, Software Engineering or related field at an accredited college/university