Data Scientist

ProPetro Energy Solutions; Houston (TX); On-site; USD 110,000 - 140,000; Full time

Job summary

PROPWR's Data Scientist will support power generation operations by developing analytics, predictive models, and data-driven insights that improve asset performance, reliability, and operational efficiency. You will work with engineering, operations, and maintenance teams to analyze large datasets from assets, SCADA, and condition-monitoring platforms.

Key tasks include building forecasting models for generation output, identifying trends and anomalies, validating data quality, and turning

Qualifications

• Bachelor's degree in Data Science, Engineering, Mathematics, Statistics, Computer Science, or related field.
• 3–7 years of experience in data science, analytics, or applied machine learning.
• Strong experience with Python or R; SQL; Time series data analysis; Experience with operational or industrial datasets.

Responsibilities

• Analyze large datasets from power generation assets, SCADA systems, sensors, and historian platforms.
• Identify trends, anomalies, and performance deviations impacting asset reliability and availability.
• Validate and improve data quality across operational systems and data pipelines.
• Develop predictive models for equipment health, failure probability, and maintenance planning.

Skills

Python

R

SQL

Time series

Operational data

Education

Bachelor's degree

Job description

Job Summary

The Data Scientist supports PROPWR’s power generation operations by developing advanced analytics, predictive models, and data‑driven insights that improve asset performance, reliability, and operational efficiency. This role works closely with engineering, operations, and maintenance teams to analyze large datasets from power generation assets, SCADA systems, and condition‑monitoring platforms. The Data Scientist transforms raw operational data into actionable insights that reduce downtime, optimize maintenance, and enhance fleet‑wide decision‑making.

Responsibilities

• Analyze large datasets from power generation assets, SCADA systems, sensors, and historian platforms.
• Identify trends, anomalies, and performance deviations impacting asset reliability and availability.
• Validate and improve data quality across operational systems and data pipelines.
• Develop predictive models for equipment health, failure probability, and maintenance planning.
• Build forecasting models for generation output, performance, and reliability.
• Support reliability engineering by providing data‑driven insights into failure mechanisms and asset risks.
• Conduct hypothesis testing and statistical analysis to validate assumptions and guide engineering decisions.
• Build dashboards, KPIs, and performance reporting tools for engineering and operations teams.
• Translate complex analytical findings into clear, actionable recommendations for non‑technical stakeholders.
• Partner with Operations, Maintenance, and Engineering to turn insights into operational improvements.
• Support root cause analysis (RCA) investigations with data‑driven evidence and modeling.
• Work with reliability engineers to integrate predictive analytics into maintenance strategies.
• Document models, assumptions, methodologies, and business impact.
• Maintain version control, code quality, and reproducibility standards.
• Support deployment of models into production environments when applicable.
• Stay current with emerging analytics tools, machine learning techniques, and power‑generation technologies.
• Maintain compliance with Health, Safety, and Environment (HSE) policies by attending all required HSE training sessions, safety meetings, and always utilizing proper Personal Protective Equipment (PPE).
• Other duties as assigned.

Position is on-site in Northwest Houston.

Job Qualifications

• Bachelor’s degree in Data Science, Engineering, Mathematics, Statistics, Computer Science, or related field.
• 3–7 years of experience in data science, analytics, or applied machine learning.
• Strong experience with Python or R; SQL Statistical modeling and machine learning techniques Time series data analysis Experience working with operational or industrial datasets.
• Ability to communicate complex analytical concepts clearly and effectively.

Preferred Qualifications

• Experience in power generation, energy, utilities, or industrial operations.
• Familiarity with SCADA, plant historian data, or condition monitoring systems.
• Experience with asset performance analytics or maintenance optimization.
• Knowledge of turbines, generators, reciprocating engines, or industrial equipment.
• Experience deploying models into production environments.
• Exposure to reliability engineering concepts (RCM, FMEA, RCA).

Physical Demands

The physical demands described here represent those required for an employee to successfully perform the role’s essential functions. Reasonable accommodation may be made for individuals with disabilities to perform their major responsibilities. While performing the duties of this job, the employee is regularly required to sit, stand, or walk; use hands to manipulate, handle, or feel; reach with hands and arms; stoop or bend; and talk or hear. The employee must occasionally lift and/or move up to 20 lbs.

Work Environment

The work environment characteristics described here represent environmental conditions an employee will encounter while performing the role’s essential functions. The noise level in some work environments can be moderate and an employee may encounter extreme weather conditions while performing major duties. Reasonable accommodation may be provided for individuals with disabilities to perform their major responsibilities.

Disclaimer

The information provided in this job description indicates the general nature and level of work performed by employees within the role’s classification. This job description is not to be interpreted as a comprehensive inventory of all duties, responsibilities, qualifications required of employees assigned to this role.

PROPWR is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.