Sustainment Data Science & Analysis Support en JSL Technologies Incorporated

Description

Sustainment Data Science & Analysis Support 

Must be in the San Diego Area (90% Remote) 

Part-time (20 hours per week)

About Us:

JSL Technologies, Inc. is a successful, growing company looking for first-rate professionals to join us in supporting the Department of Defense on cutting-edge technologies, advanced weapon systems, and innovative solutions to complex problems. We attract the best subject matter experts in Engineering, Logistics, Administrative Support, and Program Management to deliver top-notch services. Join the JSL team and earn a competitive salary, great benefits, and a rewarding career where you can make a difference. We are a Veteran-Owned company that proudly fosters an inclusive environment of diverse, ethical, committed, and highly accomplished professionals who respect each other, hold themselves accountable, and continually strive for improvement.

Job Description:

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.

Duties include:

1) Provide supportability analysis, planning, execution through life cycle. Provide acquisition design review support with sustainment focus.

2) Support system Reliability, Availability, Maintainability, Cost (RAM-C) data collection, analysis, and reporting. Develop and update Readiness Growth Candidates (RGC).

3) 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.

4) 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.

5) 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.

6) Support system reporting milestones through weekly and monthly periodic readiness and fleet operational requirements reporting. 

7) Analyze RAM-C data and develop trade off analysis and supporting decision to determine Course of Action for identified issues.

8) 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.

9) 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.

10) 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.

11) Provide weekly highlights of efforts accomplished and being worked.

Requirements

Minimum Qualifications:

· 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

Desired Qualifications:

· Active Secret security clearance.

· BS degree in Machine Learning from an accredited institution

Security Clearance:

Applicants may be required to have an active security clearance or the ability to obtain and maintain a US government security clearance as specified in the minimum qualifications. Selected candidates will be subject to a government security investigation and must meet eligibility requirements to obtain a DoD government-granted security clearance. Individuals will be subject to a background investigation to include but not be limited to, criminal history, employment and education verification, drug testing, and creditworthiness.

EEO:

JSL Technologies, Inc. is an equal opportunity employer. All applicants are considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed must be representative of the knowledge, skills, minimum education, training, licensure, experience, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Please contact [email protected] if you need accommodation for the application process.