Principal Engineer, Data Analytics and Machine Learning (hybrid - Aguadilla, Pr)

RTX RTX · Aerospace · aguadilla, Puerto Rico +1 · Engineering

Principal Engineer role focused on Data Analytics and Machine Learning in the aerospace and defense industry. The role involves analyzing complex engineering and aircraft performance datasets, developing and training ML models for predictive maintenance and forecasting, defining validation approaches, and mentoring junior engineers. Requires a strong background in mechanical engineering principles and experience with data analytics and ML methodologies.

What you'd actually do

  1. Lead and oversee the analysis of engineering and aircraft performance datasets, delivering actionable insights to improve product performance and reliability.
  2. Drive the development, training, and validation of machine learning (ML) models, with applications including predictive maintenance, service time estimation, and performance forecasting.
  3. Define and standardize validation approaches, acceptance criteria, and performance metrics for analytical and ML models across multiple programs.
  4. Guide and mentor junior engineers on data analytics and machine learning methodologies, fostering skill development and technical growth within the team.
  5. Serve as the primary technical liaison with engineering discipline owners, design teams, and external stakeholders to address critical engineering challenges and opportunities.

Skills

Required

  • Degree in Science, Technology, Engineering or Mathematics (STEM) and 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience.
  • Demonstrated professional experience communicating in English (verbal and written).
  • U.S. citizenship is required

Nice to have

  • Expertise in data analytics workflows, statistical methods, and engineering data interpretation, with a proven record of solving complex technical challenges.
  • Advanced knowledge of machine learning (ML) concepts, with demonstrated experience in developing, deploying, and validating models for prediction, classification, and anomaly detection.
  • Advanced degree in Mechanical Engineering, Aerospace Engineering, Data Science, or a related field.
  • Deep understanding of mechanical engineering principles, including structural behavior, dynamics, thermal concepts, and aircraft systems.
  • Strong experience with Python for data analysis (e.g., NumPy, Pandas, PySpark) and familiarity with ML frameworks such as TensorFlow or Scikit-learn.
  • Experience with finite element analysis (FEA), structural analysis, thermal concepts, dynamics, or instrumentation data interpretation.
  • Proficiency with database tools and SQL for manag

What the JD emphasized

  • U.S. citizenship is required
  • U.S. citizenship is required

Other signals

  • develop predictive models
  • machine learning (ML) models
  • predictive maintenance
  • service time estimation
  • performance forecasting
  • ML algorithms