Sr. Reliability Data Scientist

Ascend Performance Materials•Houston, TX

About The Position

Ascend Performance Materials is a global leader in high-performance chemicals, fibers and plastics, committed to enhancing quality of life through innovation. With fully integrated manufacturing facilities across North America, Europe, and Asia, we develop essential solutions that drive safer vehicles, cleaner energy, advanced medical devices, and durable consumer goods. Guided by a strong focus on safety, sustainability, and customer success, we operate responsibly while delivering value through cutting-edge technologies and industry expertise. Join our team and be part of a collaborative environment where your work drives meaningful global impact. The Senior Reliability Data Scientist is responsible for leveraging advanced analytics, predictive modeling, and reliability data to improve asset reliability, maintenance effectiveness, and operational performance across Ascend's manufacturing network. Reporting to the Senior Director of Reliability, this role serves as a technical expert within the Reliability organization, transforming maintenance, operational, and process data into actionable insights that reduce downtime, optimize maintenance strategies, and improve asset performance.

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.
Advanced analytical, statistical, and problem-solving capabilities.
Expert proficiency with Power BI, SQL, Python, and data visualization tools.
Experience with SAP Plant Maintenance, Maximo, or similar maintenance management systems.
Familiarity with OSIsoft PI, manufacturing historians, and industrial data architectures.
Knowledge of reliability engineering principles, predictive maintenance methodologies, and asset performance management practices.
Strong communication and presentation skills with the ability to translate complex analyses into actionable business recommendations.
Strong project management and stakeholder management capabilities.
Ability to manage multiple priorities and deliver results in a matrixed manufacturing environment.

Nice To Haves

Chemical, petrochemical, refining, energy, or other continuous manufacturing experience preferred.

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.
Apply advanced analytics, statistical techniques, and machine learning methods where appropriate to improve reliability performance.
Partner with technology, automation, and operational teams to expand digital reliability capabilities.
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.