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Internship Predictive Maintenance Jobs in Texas (NOW HIRING)

Huntsman

Mechanical Engineering Intern

Houston, TX · On-site

$18 - $24/hr

Our Summer Internship Program is designed for ambitious students who want hands-on experience in ... Analyze maintenance and reliability data * Assist with preventive and predictive maintenance ...

X-Bow Systems

Maintenance Engineer

Luling, TX · On-site

Reliability & Predictive Tools: Implement predictive tools (vibration analysis, cycle monitoring ... Experience: 2+ year of hands-on experience in a manufacturing environment (applicable internships ...

X-Bow Systems

Maintenance Engineer

Luling, TX · On-site

Reliability & Predictive Tools: Implement predictive tools (vibration analysis, cycle monitoring ... Experience: 2+ year of hands-on experience in a manufacturing environment (applicable internships ...

Xbow

Maintenance Engineer

Luling, TX

Reliability & Predictive Tools: Implement predictive tools (vibration analysis, cycle monitoring ... Experience: 2+ year of hands-on experience in a manufacturing environment (applicable internships ...

Borusan Mannesman

Automation Engineer

Baytown, TX · On-site

... internships/co-ops in manufacturing strongly preferred) 1.3. Basic understanding of PLCs and ... and predictive maintenance initiatives 2.2.4. Assist with root cause analysis of equipment and ...

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Internship Predictive Maintenance information

What is an Internship in Predictive Maintenance?

An Internship in Predictive Maintenance is a temporary position designed for students or recent graduates to gain practical experience in using data analysis, machine learning, and sensor technologies to predict equipment failures and optimize maintenance schedules in industrial settings. Interns typically assist in collecting and analyzing machine data, building predictive models, and implementing monitoring systems. This hands-on experience helps them develop technical skills relevant to the maintenance and reliability engineering fields, preparing them for future roles in industrial automation, data science, or engineering.

What are the key skills and qualifications needed to thrive as an Internship Predictive Maintenance, and why are they important?

To thrive in a Predictive Maintenance Internship, you need a background in engineering or data science, understanding of maintenance principles, and analytical problem-solving skills. Familiarity with predictive analytics tools, condition monitoring systems, and programming languages like Python or MATLAB is typically required. Strong communication, teamwork, and adaptability help interns collaborate effectively and learn quickly in dynamic industrial environments. These skills enable accurate equipment diagnostics, effective data-driven maintenance, and contribute to operational efficiency.

What is the difference between Internship Predictive Maintenance vs Predictive Maintenance Technician?

AspectInternship Predictive MaintenancePredictive Maintenance Technician
CredentialsEnrolled in or recent graduate of relevant technical or engineering programTechnical diploma or associate degree, certifications preferred
Work EnvironmentTraining setting, supervised tasks, entry-levelOn-site maintenance, hands-on equipment diagnostics
Industry UsageInternship programs in manufacturing, energy, or industrial sectorsIndustrial plants, manufacturing facilities, equipment maintenance

Internship Predictive Maintenance roles are entry-level positions designed for students or recent graduates gaining experience in predictive maintenance techniques. Predictive Maintenance Technicians are experienced professionals responsible for diagnosing and repairing equipment using predictive tools. While internships focus on learning and training, technicians perform hands-on maintenance tasks to prevent equipment failures.

What types of projects and daily tasks can I expect during an internship in predictive maintenance?

As an intern in predictive maintenance, you can expect to work closely with engineering and data analytics teams to collect, clean, and analyze equipment data. Your daily tasks may include monitoring sensor outputs, developing or refining predictive models, and supporting the implementation of machine learning algorithms to forecast equipment failures. You might also participate in cross-functional meetings to discuss findings and recommend maintenance strategies. This hands-on experience will give you exposure to both data science techniques and practical engineering solutions, helping you build valuable skills for a career in industrial analytics.
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Internship Predictive Maintenance jobs near you
Data Scientist, Smart Maintenance, & Equipment Reliability

Data Scientist, Smart Maintenance, & Equipment Reliability

Patterson-UTI

Houston, TX • On-site

Full-time

Posted 27 days ago

Patterson-UTI rating

5.0

Company rating: 5.0 out of 10

Based on 23 frontline employees who took The Breakroom Quiz

81st of 86 rated oil and gas companies

Job description

Job Summary:
Patterson-UTI is a leading provider of integrated completions that employs sustainable practices and equipment to support their customers’ ESG goals. As a Data Scientist for the Smart Maintenance initiative, you will support the creation of digital twins and predictive job models to enhance operational efficiency and reduce downtime through advanced data management and analytics.
Responsibilities:
• Support the development of predictive models and automated tracking tools to help maintenance teams shift from reactive to proactive workflows.
• Assist in the integration of equipment telemetry and various data streams into modeling frameworks to improve lifecycle management.
• Help build and test internal AI-driven tools and trend models to streamline technical troubleshooting and root cause analysis.
• Contribute to the development of cost-visibility models to track equipment spend and total cost of ownership at different fleet levels.
• Assist in the rationalization and optimization of equipment alarm systems to improve alert quality and reduce operational noise.
• Monitor the impact of system alerts to help transition toward actionable, condition-based maintenance strategies.
• Support data integrity efforts by helping to link information across disparate internal systems and work order platforms.
• Collaborate on the design of user-friendly interfaces and digital aids that provide field personnel with accurate equipment history and procedures.
Qualifications:
Required:
• Prior experience in equipment reliability, predictive maintenance or physics-based modeling in oil and gas
• Expert programming skills in Python (SciPy, NumPy) for simulation and model development
• Strong foundation in reliability engineering methods such as root cause analysis (RCA), alarm management KPIs, and failure mode modeling.
• Strong communication skills with the ability to explain complex models to non-technical stakeholders
• Ability to manage multiple priorities and deliver results on time
• Bachelor’s degree in Mechanical Engineering, Petroleum Engineering, Data Science or related field
• 0-5 years of experience applying data science modeling or reliability engineering in industrial settings
• 2+ years building and deploying data-science algorithms on cloud platforms (AWS, GCP or Azure)
• A basic understanding of maintenance workflows, work orders, and asset hierarchies is required.
Preferred:
• Prior internship or project experience involving industrial IoT sensor data and predictive maintenance is preferred.
• Master’s degree or higher in a quantitative engineering or physical science discipline
• Research publications or patents in equipment reliability, preventative maintenance or related areas
Company:
Patterson-UTI is a leading provider of drilling and completion services to oil and natural gas exploration and production companies in the United States and other select countries, including contract drilling services, integrated well completion services and directional drilling services in the United States, and specialized bit solutions in the United States, Middle East and many other regions around the world. Founded in 1978, the company is headquartered in Houston, USA, with a team of 10001+ employees. The company is currently Late Stage.

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