Data Analyst / Scientist

Reporting & Business Analytics

• 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 engineering and business leadership
• Support monthly/quarterly reporting cycles with accurate, timely data

Downtime & Reliability Data

• Collect, clean, and structure turbine downtime data from SCADA, CMMS, OEM reporting, and field logs
• Classify and root-cause downtime events (mechanical, electrical, control system, weather, grid-related, etc.)
• Maintain a reliable, queryable historical database of outage and maintenance events across the fleet

Predictive Analytics & AI Tools

• Develop and deploy predictive models (ML-based and statistical) to forecast turbine downtime and component degradation ahead of failure
• Build anomaly detection and early-warning tools using sensor/operational data (vibration, temperature, pressure, combustion parameters, etc.)
• Work with engineering to validate model outputs against physical failure modes and OEM guidance
• Iterate on models as new failure data becomes available; track model performance over time

Reliability Improvement Support

• Partner with the Reliability Manager and engineering team to identify trends driving forced outages and derates
• Support root cause analysis (RCA) efforts with data-driven insights
• Recommend maintenance interval or strategy adjustments based on data trends (RCM/predictive maintenance support)

Cross-Functional Collaboration

• Work closely with Operations, Engineering, and Asset Management to ensure data pipelines reflect real-world turbine conditions
• Present findings to technical and non-technical stakeholders, including leadership

• Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent experience)
• 2-5 years of experience in data analysis, with exposure to industrial/energy/manufacturing operations preferred
• Proficiency in SQL and Python (pandas, scikit-learn, or similar)
• Experience building dashboards (Power BI, Tableau, or similar)
• Strong understanding of statistical analysis and predictive modeling techniques
• Ability to communicate technical findings to non-technical stakeholders
• Familiarity with time-series forecasting, anomaly detection, or condition-based monitoring techniques
• Experience with SCADA/historian data (OSIsoft PI, or similar)
• Exposure to reliability engineering concepts (MTBF, RCM, FMEA)
• Experience with cloud data platforms (Azure, AWS) and ML deployment pipelines, * Exceptional communicator - direct and transparent, skilled problem-solver with proven success in building coalitions and avoiding conflicts
• Total ownership mentality - proactively identifies and removes obstacles across numerous ongoing tasks
• Independent thinker - provides original thoughts and constantly asking “how can we do this better”
• Innovative thinker - willingness to consider novel solutions and ability to adapt to change
• Desirable teammate - impeccable character, humility, and collaborative
• Relentless - aspires to contribute and achieve his/her full potential

We value your hard work, integrity, and commitment to the Solaris “First in Service & Innovation” culture through competitive pay and benefits packages and ongoing career development.

• Competitive compensation packages
• Medical, Dental & Vision benefits
• Disability Insurance
• Company paid Life and AD&D insurance with supplemental offerings
• Company matching 401(k) retirement plan
• Paid time off, including 10 paid holidays
• Career Progression
• Tuition Reimbursement

Solaris Energy Infrastructure, Inc. (NYSE:SEI) provides scalable equipment-based solutions for use in distributed power generation as well as the management of raw materials used in the completion of oil and natural gas wells. Headquartered in Houston, Texas, Solaris serves multiple U.S. end markets, including energy, data centers, and other commercial and industrial sectors.