Sustainment Data Science & Analysis Support (Part Time-20 Hours Weekly)

JSL Technologies Incorporated•Port Hueneme, CA

About The Position

JSL seeks a motivated Machine Learning student or recent graduate to fill this exciting and technically challenging part-time position in support of the US Navy. The applicant will provide support to Littoral Strike and Warfare programs including Gun Weapon System (GWS), Littoral Combat Ship (LCS) Sea Frame, LCS Mission Module (MM), LCS Trainers, Tomahawk, Unites States Coast Guard (USCG), USCG Aegies Athena program, Harpoon program, Guided-Missile Frigate (FFG) program, USS Zumwalt (DDG-1000)-Guided Missile Destroyer program, Multi-Mission Surface Combatant (MMSC) program, and Over-The-Horizon (OTH) Weapon System program, Aegis Weapon System (AWS) program, Ship Self Defense System (SSDS) program, Ballistic Missile Defense (BMD) program, Carrier program, Amphibious program, Destroyer program, Cruiser program, Frigate program, Integrated Combat System program, Unmanned program, and other programs as required.

Requirements

Ability to obtain and maintain a Secret clearance is required to be considered for this position.
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Completed college-level coursework in Machine Learning
Demonstrated computer literacy and experience with Microsoft products (Word, PowerPoint, Excel, Teams)
Demonstrated experience with JIRA, Advana Jupiter, Tableau, Python, OPUS, SIMLOX, Block SIM, R, and others

Nice To Haves

Active Secret security clearance.
BS degree in Machine Learning from an accredited institution

Responsibilities

Provide supportability analysis, planning, execution through life cycle. Provide acquisition design review support with sustainment focus.
Support system Reliability, Availability, Maintainability, Cost (RAM-C) data collection, analysis, and reporting. Develop and update Readiness Growth Candidates (RGC).
Identify and develop failure mode and root cause for systemic issues and conduct analysis to support reliability and maintainability improvements. Apply Machine Learning to reduce labor intensive data reviews.
Support development of Reliability Block Diagrams, Part to Block Diagrams, Fault Tree Analysis, Failure Modes Effectiveness and Corrective Action (FMECA), Level of Repair Analysis (LORA), and Sparing analysis. Review and assess the system design parameters and provide design, maintenance or logistics solutions.
For supportability issues, review and evaluate the ship/system concept of operations, designs and technologies, review system data deliverables and provide comments using established reporting procedures.
Support system reporting milestones through weekly and monthly periodic readiness and fleet operational requirements reporting.
Analyze RAM-C data and develop trade off analysis and supporting decision to determine Course of Action for identified issues.
Coordinate with maintenance personnel, ship riders, Original Equipment Manufacturers (OEMs), In Service Engineering Agents (ISEA), program office sponsors, and Subject Matter Experts (SME) to document and update any RAM-C issues that arise during the life cycle.
Participate in program and technical reviews to support issue identification, analysis and reporting. Utilize Navy identified systems (i.e. JIRA, Advana Jupiter, Tableau, Python, OPUS, SIMLOX, Block SIM, R, and others) to document issues, gain efficiencies, perform analysis and develop technical reports.
Support the development of key measures to determine system readiness including but not limited to: Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), and Mean Logistics Delay Time (MLDT) to generate the Operational Availability (Ao) or Material Availability (Am) or other specific measures traceable to program requirements.
Provide weekly highlights of efforts accomplished and being worked.