Data Analyst - Reliability

Overview

The Reliability Team develops and implements objective, data-driven processes to increase equipment availability and reliability to world class standards. The Reliability Program Data Analyst plays a key role in execution of the Reliability Program objectives, supporting operational continuity and excellence in a number of ways.

In this role you will

• Help identify program-specific metrics
• Identify data gaps and establish data-collection processes to close those gaps
• Analyze data using descriptive, predictive, prescriptive, and causal analyses techniques to identify impending failures and make impactful recommendations on elimination or mitigation
• Leverage PowerBI to develop and execute reporting processes for metrics
• Prepare visual aids as needed to support communication of trends and opportunities
• Investigate reliability problems relative to cost limitations for equipment availability repair/replacement costs, availability of materials and equipment, and develops plans that comply with customer demands to avoid equipment failures
• Monitor the equipment to prevent failures that could impede business operations and potentially lead to downtime or a loss of throughput
• Provide guidance on maintenance, repair, replacement, fabrication, installation, alteration, construction, and design of facility components requiring the application
• Work collaboratively with Facility Engineering for accurate and timely repair/maintenance dispositions

Other responsibilities include providing guidance on maintenance issues as needed, collaborating both internal to D962 and with other departments to support Facilities- and Maintenance-related efforts, and other tasks as needed.

Position Responsibility

• Gathers and examines reliability data from equipment (either manually or via automated methods) or other data sources and correlates data for analysis
• Descriptive statistical analysis
• Predictive statistical analysis
• Prescriptive statistical analysis
• Causal analysis (e.g. Root-cause and Root-Cause Failure Analysis)
• Failure Reporting, Analysis and Corrective Action System (FRACAS)
• Failure mode and effects analysis (FMEA)
• Other analysis tools used as needed (Weibull, reliability block diagrams, etc)
• Assist with project planning and implementation
• Work closely with budgets and finance to reduce maintenance and operating costs
• Supports execution of the facility predictable and reliable maintenance plan
• Provides input to a Risk Management Plan that will anticipate reliability-related, and non-reliability-related risks that could adversely impact plant operation
• Increase equipment longevity and reduce downtime
• Identify and mitigate risks associated with single points of failure (SPOF)
• Assists in transforming Preventative Maintenance practices into Predictive/Condition Based Maintenance.
• Works under the supervision of the Reliability Program Lead

This position is located at Quonset Point at the North Kingstown, RI location.

Qualifications

Required

• High School Diploma or Equivalent
• Minimum 4 years of maintenance-related data analysis
• Minimum 3 years of experiene managing PowerBI queries and reporting dashboards
• Must be able to work all shifts

Preferred

• Formal training in maintenance-related data analysis
• Working history with computerized maintenance management systems or enterprise asset management systems
• Experience with root cause analysis techniques
• Lean/Six Sigma Black Belt certification
• Military experience

Skills

• Exceptional organizational skills
• Strong leadership, communication, and interpersonal skills
• Applicant will be required to work in a collaborative and team environment and meet organizational goals
• Exceptional customer service skills
• Strong teamwork and problem solving skills
• Familiarity with Six Sigma and project management methodologies
• Analytical skills

Physical Qualifications

Climbing, Kneeling, Light Lifting, Medium Lifting, Pulling, Pushing, Reaching, Stooping, Twisting

Environmental Attributes

Cold, Dry, Dusty, Fumes, Hot, Inside, Noise, Oily, Outside, Wet