Director, Data Science and Machine Learning

Beehive Industries is dedicated to Powering American Defense by revolutionizing the design, development, and delivery of jet propulsion systems to support the warfighter. Through the integration of additive manufacturing, the company aims to meet the growing and urgent needs for unmanned aerial defense by dramatically improving a jet engine’s speed to market, fuel efficiency, and cost.  

Founded in 2020, the company is headquartered in Englewood, Colorado, with additional facilities in Knoxville, Tennessee, Loveland Ohio, and Mount Vernon, Ohio. Beehive is committed to grow and advance the defense industrial base while manufacturing exclusively in the USA. This role will be located at our Loveland, Ohio facility.

Role Overview

The Head of Data Science & Machine Learning will lead the company’s enterprise-wide efforts to unlock the value of data in aerospace design, manufacturing, operations, and business development. This role is responsible for building and scaling a high-performing data science and machine learning team, advancing digital thread initiatives, and ensuring alignment with aerospace and defense industry standards for safety, security, and compliance. The leader will partner with engineering, manufacturing, supply chain, and defense customers to deliver advanced analytics solutions that drive operational efficiency, predictive maintenance, design optimization, and mission readiness.

What You'll Do...

• Strategic Leadership and Influence
• • Define and execute the company’s data science and ML strategy in alignment with Digital Office transformation objectives
  • Champion and influence the use of AI/ML across engineering, manufacturing, and operations to drive competitive advantage
  • Act as the technical authority on data innovation, advanced analytics, and responsible AI
• Technical Delivery & Capability Development
• • Build and lead a multidisciplinary team of data scientists, ML engineers, and data analysts
  • Establish best practices for model development, deployment, and monitoring
  • Develop workforce training programs to upskill teams in AI/ML adoption
• Innovation & Delivery
• • Design, develop, and deploy ML models for use cases such predictive maintenance, quality assurance automation, digital twin optimization, and supply chain resilience
  • Oversee development of scalable ML pipelines and integration from model-based design to engineering systems and ERP/MES platforms
  • Partner with product engineering teams to embed ML models into aircraft design and manufacturing processes
  • Build production-ready ML pipelines, ensuring reliability, scalability, and security in aerospace environments
  • Conduct exploratory data analysis and applied research to uncover opportunities for operational efficiency and innovation
• Governance & Compliance
• • Ensure compliance with aerospace and defense standards (NIST, DoD, FAA, ITAR)
  • Define frameworks for data governance, ethics, and explainability in AI/ML applications
  • Partner with IT to manage intellectual property and data security considerations for sensitive defense and aerospace programs
• Cross-Functional Collaboration
• • Partner with engineering, operations, and IT leaders to align ML initiatives with business priorities
  • Engage with customers and government partners to translate mission requirements into data-driven solutions
  • Partner with external academic, industry, and defense labs to accelerate innovation
  • Facilitate workshops and proof-of-concept efforts to demonstrate AI/ML value

You have...

• Advanced degree (PhD or Master’s) in Computer Science, Data Science, Engineering, Applied Mathematics, or related field
• 7+ years of applied data science and machine learning experience, preferably in aerospace, defense, or safety-critical industries
• SQL/Python/Typescript or equivalent
• Proven track record of delivering AI/ML solutions from prototype to production in complex industrial environments using Agile methodologies
• Strong expertise in ML frameworks (ex: TensorFlow, PyTorch, scikit-learn), data