Factory Intelligence Principal Data Scientist (Hybrid)

Prattwhitney; Tucson (AZ); Hybrid; USD 107,500 - 204,500; Full time

Benefits offered by this job

Medical, dental, vision

401(k) match

Paid time off

Job summary

RTX is seeking a Principal Data Scientist – Factory Intelligence in Tucson, AZ, with hybrid work options. You will lead production-grade ML efforts, mentor junior data scientists, and partner with Engineering, Operations, and Quality teams to translate data into actionable insights.

The role requires strong Python/SQL development, ML deployment experience, and security clearance capabilities. You will contribute to scalable analytics standards and data-driven manufacturing improvements.

Qualifications

• University degree or equivalent; advanced degree preferred.
• Minimum 8 years of relevant experience, or 5+ with advanced degree.
• Experience deploying and scaling ML models in production environments.
• Strong Python and SQL development for production-grade analytics.

Responsibilities

• Transform factory data into actionable intelligence to improve production performance.
• Collaborate with Engineering, Operations, and Quality teams to deploy predictive analytics solutions.
• Develop models that improve yield, reduce variation, and support smart decisions.
• Design, deploy, and maintain production-grade ML solutions.
• Build intuitive data visualization tools and statistical analysis apps.

Skills

Leadership

Mentoring

Data storytelling

Cross-functional collaboration

Education

University degree or equivalent

Advanced degree

Tools

Python

SQL

MLflow

PyTorch/TensorFlow

AWS/Azure

Job description

RTX is seeking a Principal Data Scientist – Factory Intelligence in Tucson, AZ, with hybrid work options. You will lead production-grade ML efforts, mentor junior data scientists, and partner with Engineering, Operations, and Quality teams to translate data into actionable insights.

The role requires strong Python/SQL development, ML deployment experience, and security clearance capabilities. You will contribute to scalable analytics standards and data-driven manufacturing improvements.