Honeywell is a Fortune 100 company that invents and manufactures technologies to address critical challenges linked to global macrotrends such as safety, security, productivity, global urbanization and energy. With approximately 129,000 employees worldwide, including more than 19,000 engineers and scientists, Honeywell has an unrelenting focus on quality, delivery, value, and technology in everything they make and do. Honeywell has been named a Top 100 Global Innovator for seven years in a row, recognizing the company’s global reach of portfolio and invention influence. 

In Kansas City, Honeywell Federal Manufacturing & Technologies (FM&T) manages and operates the U.S. Department of Energy/National Nuclear Security Administration’s (NNSA) Kansas City National Security Campus. This state-of-the-art engineering, manufacturing, and sourcing facility produces a wide array of intricate components to deliver trusted national security products and government services primarily for the NNSA. Honeywell FM&T’s culture of integrity, commitment and continuous improvement enables them to deliver responsive, collaborative, and innovative management and technology services and products that translate into cutting edge solutions to complex national security issues.

This individual will contribute their expertise in data science to a multi-functional team implementing an high-visibility manufacturing initiative at the Kansas City National Security Campus (KCNSC). The primary focus on this role will be generating, validating, and deploying predictive models for scheduling and expected manufacturing flow times.

The selected candidate will:

• Interface with multiple stakeholders and team members to improve predictability of operations
• Work with internal web application development team accessible throughout KCNSC to incorporate established models into stakeholder daily decision making processes
• Work with data science team to provide solutions to stakeholder challenges on a variety of topics
• Stay current with relevant emerging technology and make recommendations on investment opportunities
• Mentor team members and provide guidance on data science topics improve the effectiveness of the team