Senior Data Scientist

Union Technologies is reindustrializing America’s defense manufacturing base with a first-of-its-kind Factories-as-a-Stockpile™ model, integrating advanced robotics, manufacturing, and software into sovereign factory systems at scale. Headquartered in Dallas, Texas, UNION is dedicated to restoring America’s industrial strength and guaranteeing deterrence for the century ahead.

We recently emerged from stealth with $50M in funding led by top-tier venture investors and strategic partners. Read more in our Press Release

Job Summary:

We need you to help unlock the power of data in defense manufacturing - building AI-driven insights that predict perfection and prevent failures in high-stakes production.

Have you developed machine learning models in manufacturing or aerospace settings? Do you thrive on dissecting massive datasets to forecast issues and boost quality? Have you turned statistical analyses into real-world process wins using tools like Python and Power BI?

Do you want to be part of a culture that demands data-backed excellence, where every insight fortifies national security and operational efficiency? We need a seasoned Senior Data Scientist to develop advanced analytics and machine learning models for optimizing manufacturing processes and product quality in defense-grade steel forgings.

With 5–7 years of experience in data science or advanced analytics within manufacturing, aerospace, or defense, plus expertise in Python, R, SQL, Minitab, R-Shiny, Power BI, machine learning, predictive modeling, SPC, CpK/PpK analysis, and root cause tools like 8D and 5-Why.

Come work in our cutting-edge facility and help us integrate data-driven strategies with Lean and Six Sigma principles to foster a collaborative, innovative environment that automates excellence and mentors the next generation of analysts.

Key Responsibilities:

• Develop advanced analytics and machine learning models to optimize manufacturing processes and improve product quality for defense-grade steel forgings.

• Analyze large-scale production and quality datasets to identify trends, predict failures, and drive process improvements.

• Apply statistical methods (CpK/PpK, SPC) and predictive modeling to ensure process capability and stability.

• Build dashboards and visualization tools using Python, R-Shiny, and Power BI for real-time process monitoring.

• Collaborate with process engineers to integrate data-driven insights into shop-floor operations.

• Implement root cause analysis using data (8D, 5-Why) and support continuous improvement initiatives.

• Drive automation of data collection and reporting systems to reduce manual effort and improve accuracy.

• Serve as a technical mentor for junior data analysts and engineers on statistical and machine learning techniques.

Required Qualifications:

• Education: Bachelor’s or Master’s in Data Science, Statistics, Computer Science, Industrial Engineering, or related field.

• Experience: 5–7 years in data science or advanced analytics within manufacturing, aerospace, or defense; experience with process optimization preferred.

Technical Skills:

• Programming: Python, R, SQL

• Tools: Minitab, R-Shiny, Power BI, Excel

• Methods: Machine Learning, Predictive Modeling, SPC, CpK/PpK analysis

Work Style: Analytical thinker; hands-on problem solver; thrives in high-pressure environments.

Soft Skills: Strong communicator; collaborative; able to translate complex data insights into actionable recommendations.

Preferred Qualifications:

• Familiarity with defense standards (MIL-STD, ITAR).

• Experience with manufacturing data systems (MES, ERP) and IoT sensor data.

• Knowledge of Six Sigma and Lean principles.

• Exposure to cloud platforms (AWS, Azure) for data pipelines and model deployment.

Join the mission. We'll handle the move.

Exceptional talent shouldn't be bound by geography. If you're ready to relocate to Dallas, TX and join us in reindustrializing America’s defense backbone, we'll make it easy—and for those who move quickly, additional incentives may be available.