Senior Reliability Data Scientist

Jobtailor; Alabama; On-site; USD 90,000 - 140,000; Full time

Job summary

Jobtailor is seeking an experienced reliability analytics professional to lead data-driven initiatives across maintenance and operations in Alabama. The role focuses on predictive maintenance, asset performance, and reliability governance to drive measurable improvements.

Ideal candidates bring a decade of experience, strong analytical skills, and the ability to influence cross-functional teams without direct authority.

Qualifications

• Bachelor's degree in a technical field; advanced degree preferred.
• 10+ years of experience in reliability or manufacturing analytics.
• Proven ability to develop predictive models and advanced analytics.
• Experience with manufacturing, maintenance, and historian data.
• Experience supporting predictive maintenance and asset performance management.
• Ability to influence cross-functional teams without direct authority.
• Experience in chemical, petrochemical, refining, or continuous manufacturing preferred.

Responsibilities

• Analyze equipment data to identify reliability risks and improvement opportunities.
• Develop and maintain reliability dashboards and KPIs to support data-driven decisions.
• Conduct reliability studies and failure trend evaluations.
• Provide actionable insights to reliability, maintenance, engineering, and operations teams.
• Support predictive maintenance programs for critical assets.
• Develop asset health monitoring methodologies and predictive indicators.
• Evaluate effectiveness of predictive maintenance technologies.
• Develop predictive analytics and automated alerts for equipment health.
• Use data to identify patterns and performance improvement opportunities.
• Support reliability governance and investment decision reporting.

Skills

Reliability analytics

Predictive models

Advanced analytics

Digital solutions

Data science

Education

Bachelor's degree in Engineering or related field

Advanced degree preferred

Job description

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.

Requirements

• 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.