About this role
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