Data Scientist, Smart Maintenance, & Equipment Reliability

Key Responsibilities

• Support the development of predictive models and automated tracking tools to help maintenance teams shift from reactive to proactive workflows.
• Aid 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.

Job Requirements

• 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.

Minimum Qualifications

• 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 Qualifications

• Prior internship or project experience involving industrial IoT sensor data and predictive maintenance.
• 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.