Data Scientist

PROPWR•Houston, TX

About The Position

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. The role works with engineering, operations, and maintenance teams to analyze datasets from power generation assets, SCADA systems, and condition-monitoring platforms, turning operational data into insights intended to reduce downtime, optimize maintenance, and improve fleet-wide decision-making. The position also supports reliability engineering, root cause analysis, model deployment, and the integration of predictive analytics into maintenance strategies. The employee documents models and methods, maintains code quality and reproducibility standards, and keeps current with analytics tools, machine learning techniques, and power-generation technologies. HSE policies must be followed, including attendance at required training and safety meetings and use of proper PPE.

Requirements

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

Nice To Haves

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 such as RCM, FMEA, and RCA.

Responsibilities

Analyze large datasets from power generation assets, SCADA systems, sensors, and historian platforms.
Identify trends, anomalies, and performance deviations affecting 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.
Provide data-driven insights into failure mechanisms and asset risks to support reliability engineering.
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 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 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.
Follow HSE policies by attending required training sessions and safety meetings and using proper PPE.
Perform other duties as assigned.