Sr. Principal Systems Engineer- Prognostics / PHM Northrop Grumman

Northrop Grumman Aeronautics Systems is looking to add a Sr. Principal Systems Engineer (Prognostics and Health Management) to our team in Melbourne, FL. The ideal candidate should have a background in Health Management (HM), with a particular focus on Prognostics and Diagnostic processes, analysis, and design for integrated systems. The candidate should also have a strong foundational understanding of Reliability and Maintainability (R&M) engineering analyses and processes Essential Functions: This position requires proven experience integrating inputs from multiple systems to generate an integrated architecture; identify, develop, and decompose requirements; developing simple and complex logic or rulesets which can be used in Prognostic and Diagnostic Algorithms; and analyze flight and sensor logs for anomalous behavior and identifying root cause to reduce troubleshooting and increase fault isolation. Analyses are performed at all stages of the complete system lifecycle to include: concept, design, fabrication, test, installation, operation, maintenance and disposal. Candidate will be expected to perform Built in Test (BIT) analysis, timeline analyses, detailed trade studies, sensor placement assessments, Mission Systems software analyses, and interface definition studies to translate customer requirements into hardware and software specifications. Job Duties: Analyze various types of flight/fault logs, assign Fault Identification (FID) codes, and host customer BIT Review Boards (BRB) Coordinate with stakeholders, suppliers, and customers, internally and externally, in support of improving Built-In-Test (BIT) Fault Detection, Fault Isolation and BIT False Alarm performance metrics Develop models and scalable algorithms derived from both data and underlying physics of failure models to evaluate the condition of Mission and Air Vehicle systems Create predictive models of physical degradation, failures, and data-driven prognostics algorithms to assess the health and performance of critical components Formulate health monitoring strategies to detect anomalies/outliers in real flight data Conduct research and development projects concentrating on Prognostic Health Management (PHM) and Condition Based Maintenance Plus (CBM+) Gather and analyze component and WRA failure history, including failure modes, downtime, MTBF, power cycles, reboots, repeat faults, etc. Address complex questions regarding fleet usage and behavior to facilitate proactive monitoring, enhance reliability, and minimize field failures Effectively communicate and present to project and program management, and other technical and non-technical managers and customers Work collaboratively in a team environment with other highly motivated system engineers and data scientists Basic Qualifications: Bachelors Degree from an accredited college in a relevant STEM discipline with 8 years of experience; OR a Masters Degree in a STEM field with 6 years of experience; OR a STEM PhD with 3 years of experience Experience within Health Management, Prognostics, Diagnostics, and/or Reliability & Maintainability Engineering disciplines Previous experience, including academic research, directly related to the development of Prognostics Health Management (PHM) or Condition Based Maintenance Plus (CBM+) technologies, or analysis and simulation of complex electrical or mechanical systems Strong background in data analysis (algorithms, data structures, and architectures), probability, statistics, signal processing, and predictive modeling Work experience with anomaly/outlier detection in time series data Must have an active DoD Secret clearance prior to starting, along with the ability to obtain and maintain a Top-Secret clearance Must have the ability to obtain and maintain Special Program Access (PAR) Proficiency in Microsoft Visio, Project, Word, PowerPoint, and Excel Office Products Preferred Qualifications: Strong programming skills, preferably in Python and its numerical and data libraries (pandas, scipy, numpy, etc.) Work experience with big data tools (Databricks, Presto, Data Lake, Apache Spark, etc.) Work experience developing visualization tools and dashboards using Tableau Familiarity with fault detection and diagnosis methods, and reliability analysis Experience with MLOps and building machine learning pipelines in a professional setting Demonstrated history of generating new ideas or improving existing ideas in statistical modeling or machine learning, indicated by accomplishments such as first-author publications or projects Experience with data architectures in relation to how to store, fetch, and manipulate data (SQL, custom APIs, etc.) Masters degree in a relevant STEM field. Possess an active Top-Secret (or higher) clearance.

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