Principal Engineer, Data Analytics and Machine Learning (Hybrid - Aguadilla, PR)

Prattwhitney; Aguadilla (PR); Hybrid; USD 110,000 - 150,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Three weeks of vacation for newly hired employees

Generous 401(k) plan with employer matching

Life insurance and disability coverage

Employee Assistance Plan with free counseling sessions

Job summary

Prattwhitney in Aguadilla, PR, is seeking a Principal Engineer for Data Analytics & Machine Learning for Mechanical Systems. This hybrid position involves leading various data analyses and machine learning model development to enhance product performance and reliability.

The ideal candidate will have over 8 years of relevant experience in STEM fields, advanced knowledge of Python, and the ability to communicate effectively in English. We offer extensive benefits including medical insurance and a generous 401(k) plan.

Qualifications

• 8+ years of relevant experience, or 5+ years with an advanced degree.
• Demonstrated professional communication skills in English (verbal and written).
• U.S. citizenship required.

Responsibilities

• Lead analysis of engineering and aircraft performance datasets.
• Drive development of machine learning models for predictive maintenance.
• Guide and mentor junior engineers on data analytics methodologies.
• Serve as technical liaison with various stakeholders.

Skills

Data analytics workflows

Machine learning concepts

Python for data analysis

Statistical methods

Advanced data visualization

Agile methodologies

Education

Bachelor’s or Master’s degree in STEM

Tools

SQL

Tableau

Power BI

TensorFlow

PySpark

JIRA

Confluence

Job description

Date Posted: 2026-05-21
Location: Aguadilla, PR

Position

Principal Engineer – Data Analytics & Machine Learning for Mechanical Systems (Hybrid)

What You Will Do

• Lead and oversee the analysis of engineering and aircraft performance datasets, delivering actionable insights to improve product performance and reliability.
• Drive the development, training, and validation of machine learning models for predictive maintenance, service time estimation, and performance forecasting.
• Define and standardize validation approaches, acceptance criteria, and performance metrics for analytical and ML models across multiple programs.
• Guide and mentor junior engineers on data analytics and machine learning methodologies, fostering skill development within the team.
• Serve as the primary technical liaison with engineering discipline owners, design teams, and external stakeholders to address critical engineering challenges.
• Develop best practices for data curation, cleaning, integration, and preparation for analytics and ML workflows.
• Oversee the analysis of strain gauge, structural, thermal, and flight/aircraft data to identify trends and predictive indicators.
• Collaborate with design, reliability, service engineering, and customer support teams to provide data‑driven recommendations that enhance product performance.
• Conduct advanced statistical analyses to assess quality, completeness, and integrity of engineering and enterprise data.
• Research, evaluate, and implement cutting‑edge ML algorithms (e.g., supervised learning, unsupervised learning, clustering, anomaly detection) to solve engineering challenges.
• Represent the organization in executive‑level presentations, communicating technical recommendations and insights to leadership and customers.
• Develop and maintain technical documentation, presentations, and reports for technical and non‑technical audiences.
• Advocate for and lead digital transformation efforts, leveraging digital thread, PLM systems, and model‑based engineering workflows.
• Occasionally travel domestically and/or internationally to support project requirements, supplier engagements, or customer needs.

Qualifications

• Bachelor’s or Master’s degree in Science, Technology, Engineering or Mathematics (STEM).
• 8+ years of relevant experience, or 5+ years with an advanced degree.
• Demonstrated professional communication skills in English (verbal and written).
• U.S. citizenship required.

Preferred Qualifications

• Expertise in data analytics workflows, statistical methods, and engineering data interpretation.
• Advanced knowledge of machine learning concepts and experience deploying and validating models for prediction, classification, and anomaly detection.
• Advanced degree in Mechanical Engineering, Aerospace Engineering, Data Science, or related field.
• Deep understanding of mechanical engineering principles: structural behavior, dynamics, thermal concepts, and aircraft systems.
• Strong experience with Python for data analysis (NumPy, Pandas, PySpark) and familiarity with TensorFlow or Scikit‑learn.
• Experience with finite element analysis (FEA), structural analysis, thermal dynamics, or instrumentation data interpretation.
• Proficiency with database tools and SQL.
• Advanced experience with data visualization tools (Tableau, Power BI).
• Experience with Agile methodologies and tools (JIRA, Confluence) and cross‑functional collaboration.
• Knowledge of digital thread, PLM systems, or model‑based engineering workflows.

What We Offer

• Medical, dental, and vision insurance.
• Three weeks of vacation for newly hired employees.
• Generous 401(k) plan with employer matching.
• Participation in the Employee Scholar Program (ESP).
• Life insurance and disability coverage.
• Employee Assistance Plan with up to 8 free counseling sessions.

Equal Opportunity Employer

RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.

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