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

Date Posted:

2026-04-22

Country:

United States of America

Location:

US-PR-AGUADILLA-110 ~ Rd 110 N Km 28.8 ~ RD110

Position Role Type:

Hybrid

U.S. Citizen, U.S. Person, or Immigration Status Requirements:

U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Security Clearance:

None/Not Required

At Collins Aerospace, we design, invent, and deliver advanced aerospace and defense solutions that keep the world moving. Our Global Engineering & Technology Center in Puerto Rico (GETC-PR) is looking to hire a talented Mechanical Engineer with foundational skills in Data Analytics and Machine Learning to support engineering teams by analyzing aircraft services and engineering datasets. This role focuses on extracting insights, building predictive models, and contributing to field performance and reliability improvements.

A strong understanding of mechanical engineering fundamentals, such as structural behavior, dynamics, or thermal concepts, will help the candidate interpret engineering data while collaborating effectively with subject matter experts. This analytics-focused role works closely with engineering discipline owners, providing opportunities for professional growth and impactful contributions.

This position will sit at our Aguadilla, PR location. You must be residing in Puerto Rico at the time of starting employment. Relocation is not offered.

This role is categorized as hybrid, with 3 days onsite and 2 days remote following the schedule assigned by the Manager.

What YOU will do:

Analyze engineering and aircraft performance datasets to generate actionable insights for product performance and reliability.

Perform data curation, cleaning, integration, and preparation for analytics, modeling, and machine learning workflows.

Work with strain gauge, structural, thermal, and flight/aircraft data to extract features, identify trends, and detect anomalies or predictive indicators.

Develop, train, and validate machine learning (ML) models for applications such as predictive maintenance, service time estimation, and performance forecasting.

Define validation approaches, acceptance criteria, and performance metrics for analytical and ML models.

Provide data-driven insights and recommendations to design, reliability, service engineering, and customer support teams.

Conduct statistical analyses to evaluate the quality and completeness of engineering or enterprise data.

Collaborate with engineering teams to support investigations and continuous improvement initiatives using digital thread and enterprise data resources.

Research and implement ML algorithms (e.g., supervised learning, unsupervised learning, clustering, anomaly detection) for engineering applications.

Prepare technical documentation, presentations, and reports for technical and non-technical audiences.

Occasionally travel domestically and/or internationally to support project requirements.

What YOU will learn:

You will learn about our growing engineering team in Puerto Rico; What we do? Who we support? How we work?

You will learn the technologies of today and tomorrow which we count on to maintain world leadership in the aerospace industry.

You will learn why people enjoy and feel fulfilled by working in our industry.

Qualifications you must have:

Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and 2 years prior relevant experience or an Advanced Degree in a related field.

Demonstrated professional experience communicating in English (verbal and written).

U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Qualifications We Prefer:

Degree in Mechanical Engineering, Aerospace Engineering, or a related field

Experience in data analytics workflows, statistical methods, and engineering data interpretation (internship/co-op experience qualifies).

Hands-on experience with Python for data analysis (e.g., NumPy, Pandas, PySpark).

Basic knowledge of machine learning concepts, with experience in developing and validating models for prediction, classification, or anomaly detection.

Understanding of mechanical engineering principles, including structural behavior, dynamics, and thermal concepts, or knowledge of aircraft systems.

Proficiency with Microsoft Office programs and tools.

Familiarity with database tools and SQL.

Exposure to data visualization tools (e.g., Tableau, Power BI).

Background in finite element analysis (FEA), structural analysis, thermal concepts, dynamics, or instrumentation data interpretation (not required, but beneficial).

Experience with Agile methodologies and tools (e.g., JIRA, Confluence).

Exposure to digital thread, PLM systems, or model-based engineering workflows.

What We Offer

Some of our competitive benefits package includes:

Medical, dental, and vision insurance

Three weeks of vacation for newly hired employees

Generous 401(k) plan that includes employer matching funds

Participation in the Employee Scholar Program (ESP)

Life insurance and disability coverage

Employee Assistance Plan, including up to 8 free counseling sessions.

And more!

Collins Aerospace, an RTX business, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers’ toughest challenges and to meet the demands of a rapidly evolving global market.

Join our growing engineering team in Puerto Rico, where you will provide critical support to all Collins SBUs, working on exciting programs and projects ranging from the development of the next generation of advanced concept ejection seats to the latest technologies for the U.S. warfighter.

WE ARE REDEFINING AEROSPACE.

Please consider the following role type definition as you apply for this role.

Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.

At Collins, the paths we pave together lead to limitless possibility. And the bonds we form – with our customers and with each other - propel us all higher, again and again.