Data Scientist

PROPWR•Houston, TX

23d•Onsite

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. 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.

Requirements

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.

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 (RCM, FMEA, RCA).

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 utilize proper Personal Protective Equipment (PPE)
Other duties as assigned