Naval Reliability & Sustainment Data Scientist

JSL Technologies Incorporated•Port Hueneme, CA

20h•$75,000 - $100,000•Onsite

About The Position

JSL Technologies is seeking a Naval Reliability & Sustainment Data Scientist to deliver advanced RAM-C analytics, predictive maintenance modeling, and supportability engineering for U.S. Navy surface combat systems and weapons programs. Operating directly at the Port Hueneme Division (PHD NSWC) government site, you will leverage fleet maintenance data, statistical analysis, and machine learning models to identify system failure trends, optimize readiness metrics (MTBF, MTTR, MLDT), and directly influence naval acquisition and sustainment decisions.

Requirements

Must be legally authorized to work in the United States without the need for employer sponsorship now or at any time in the future.
Ability to obtain and maintain an active U.S. DoD Secret clearance.
Must work on-site at the Naval Surface Warfare Center Port Hueneme Division (PHD NSWC).
Proven experience working with complex maintenance, logistics, or engineering datasets using analytics platforms.
Understanding of RAM-C principles, root-cause analysis (RCA), and lifecycle supportability concepts.
Ability to distill complex datasets into clear briefings, technical reports, and actionable recommendations for program leads.

Nice To Haves

Prior support for Navy combat systems, surface ships, or ISEA operations.
Proficiency in Python (pandas, scikit-learn, numpy), Tableau visualization, Jira, and DoD enterprise data platforms like Advana / Jupiter.
Direct experience creating FMECAs, LORAs, Fault Trees, and spare parts optimization models.
Hands-on application of predictive maintenance analytics or Agile development frameworks in defense environments.

Responsibilities

Extract, clean, and analyze readiness, maintenance, and operational data across Navy platforms using tools like Python, Tableau, and Advana Jupiter.
Identify systemic failure modes and root causes using statistical methods and Machine Learning (ML). Develop predictive models to improve material availability ($A_m$) and operational readiness ($A_o$).
Develop and update Reliability Block Diagrams (RBDs), Fault Tree Analyses (FTAs), FMECAs, Level of Repair Analyses (LORAs), and sparing models.
Track key performance indicators—including Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Mean Logistics Delay Time (MLDT)—and deliver weekly/monthly executive dashboards and technical reports.
Participate in system design and engineering reviews to evaluate new technologies and concepts of operation for long-term sustainment impacts.
Collaborate with In-Service Engineering Agents (ISEAs), OEMs, program sponsors, and fleet maintenance crews to resolve systemic RAM-C deficiencies.
Use Jira and custom workflows to document data engineering pipelines, track issue resolution, and report metrics to Navy leadership.