Senior Reliability Data Scientist

Responsibilities

• Analyze equipment, maintenance, and operational data to identify reliability risks, failure trends, bad actors, and improvement opportunities.
• Develop and maintain reliability dashboards, scorecards, and key performance indicators that support data‑driven decision-making.
• Conduct statistical analyses, reliability studies, and failure trend evaluations to support reliability improvement initiatives.
• Provide actionable insights to reliability, maintenance, engineering, and operations teams to improve asset performance.
• Support the development and optimization of predictive maintenance programs for critical manufacturing assets.
• Analyze condition monitoring data, asset criticality rankings, and maintenance strategies to improve equipment reliability and maintenance effectiveness.
• Develop asset health monitoring methodologies, predictive models, and leading indicators of equipment failure.
• Evaluate the effectiveness of predictive maintenance technologies and identify opportunities for continuous improvement.
• Develop predictive analytics, automated alerts, equipment health scoring systems, and reliability monitoring tools.
• Utilize manufacturing, maintenance, and historian data to identify patterns, risks, and performance improvement opportunities.
• Support reliability improvement initiatives through root cause analysis, performance tracking, and benefit realization reporting.
• Develop reporting and analytics that support reliability governance, prioritization, and investment decisions.
• Promote best practices and standard methodologies across reliability programs and manufacturing sites.
• Quantify operational and financial benefits resulting from reliability and maintenance improvements.

• Bachelor's degree in Engineering, Data Science, Statistics, Applied Mathematics, Computer Science, Industrial Engineering, or related technical discipline; advanced degree preferred.
• Minimum 10 years of experience in reliability analytics, manufacturing analytics, data science, reliability engineering, maintenance engineering, or related industrial roles.
• Demonstrated experience developing predictive models, advanced analytics, and digital solutions that improve asset reliability and operational performance.
• Experience working with manufacturing, maintenance, operational, and historian data in complex industrial environments.
• Experience supporting predictive maintenance, asset performance management, reliability improvement, or operational excellence programs.
• Experience influencing cross‑functional teams and driving improvements without direct authority.
• Chemical, petrochemical, refining, energy, or other continuous manufacturing experience preferred.