Data Scientist: Turbine Reliability & Predictive Analytics

Solaris Oilfield Infrastructure, LLC; Houston (TX); On-site; USD 90,000 - 140,000; Full time

Benefits offered by this job

Disability Insurance

Life and AD&D Insurance

401(k) Retirement Plan

Paid time off

Tuition Reimbursement

Job summary

Solaris Energy Infrastructure, Inc. is seeking a mid-level Data Scientist to support turbine operations and reliability. You will bridge data engineering, modeling, and turbine knowledge to improve fleet reliability and provide visibility to leadership.

The role involves building dashboards for business stakeholders and developing predictive models for equipment health, with opportunities to work across Operations, Engineering, and Asset Management.

Qualifications

• Bachelor's degree in a related field or equivalent experience.
• 2–5 years of data analysis in industrial/energy/manufacturing ops preferred.
• Proficiency in SQL and Python (pandas, scikit-learn).
• Experience building dashboards (Power BI, Tableau).
• Knowledge of time-series forecasting and anomaly detection.

Responsibilities

• Build and maintain P&L dashboards tracking turbine fleet financial performance, availability, and cost drivers.
• Translate operational data into clear financial and operational KPIs for leadership.
• Support monthly/quarterly reporting with accurate data.
• Develop predictive models to forecast downtime and degradation.
• Collaborate with engineering to validate model outputs against OEM guidance.

Skills

SQL

Python

Dashboards

Time-series analysis

Communication

Education

Bachelor's degree in Data Science / Statistics / CS

Tools

Power BI

Tableau

OSIsoft PI / SCADA data

Azure / AWS

Job description

Solaris Energy Infrastructure, Inc. is seeking a mid-level Data Scientist to support turbine operations and reliability. You will bridge data engineering, modeling, and turbine knowledge to improve fleet reliability and provide visibility to leadership.

The role involves building dashboards for business stakeholders and developing predictive models for equipment health, with opportunities to work across Operations, Engineering, and Asset Management.