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As a Data Scientist for the Smart Maintenance initiative, you will support the creation of digital twins and predictive job models to reduce operational uncertainty and strengthen uptime during dynamic operations. You will help modernize maintenance and failure workflows to uncover subtle trends that traditional evaluations may miss, helping the company pursue a meaningful asset-life advantage. By moving data automatically across systems and removing reliance on manual reconciliation, you will help build a foundation for real-time maintenance management and total cost of ownership visibility. Your work will empower field teams with consolidated, purpose-built data that transforms every intervention into a precise, closed-loop maintenance event.

Key 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.  

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 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