The individual that fills this role will be responsible for applying data science and engineering principles to develop data architectures and predictive models in support of program CBM+ initiatives. The individual will coordinate closely with the Reliability and Maintenance-Cost (RAM-C) team in support of CBM+ architecture delivery by WS Critical Design Review (CDR), and predictive model delivery on Reliability Centered Maintenance (RCM)-selected components by System Quality and Verification Review (SQVR).

Position Benefits:

As a full-time employee of Northrop Grumman Space Systems, you are eligible for our robust benefits package including:??

Medical, Dental & Vision coverage??

401k??

Educational Assistance??

Life Insurance??

Employee Assistance Programs & Work/Life Solutions ??

Paid Time Off ??

Health & Wellness Resources ??

Employee Discounts??

This position’s standard work schedule is a 9/80. The 9/80 schedule allows employees who work a nine-hour day Monday through Thursday to take every other Friday off. This role may offer a competitive relocation assistance package.

Key Responsibilities:

Develop data architectures in support of predictive modeling for a large-scale WS designed to be operational for multiple generations.

Create statistical, ML, and physics-based predictive models that forecast the Remaining Useful Life (RUL) of Line Replaceable Units (LRU) identified in CBM+ trade studies.

This includes processes like data exploration, model feasibility/accuracy evaluation, cost evaluation, model development & validation, and model demonstration.

Establish templates and standards for modeling and tracking CBM+ items and processes.

Support development of the CBM+ Program Plan.

Collaborate with both NG and customer data scientists to define a data architecture for the WS; ensure the CBM+ tech stack remains aligned with wider architecture tools and plans.

Engage directly with the customer in support of evolving program requirements.

Typical Minimum Education or Experience
•    T04: 8 Years with Bachelors in Science; 6 Years with Masters; 4 Years with PhD. 

Senior Principal Data Scientist: Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, Mechanical/Aerospace Engineering, or a related STEM field and 8 years of experience; OR a Master’s degree and 6 years of experience; OR a PhD and 4 year of experience. 

U.S. Citizen and Ability to obtain a DoD Secret clearance.

2+ years of demonstrated experience developing cloud-based data architectures.

2+ years of demonstrated experience designing and implementing end-to-end data pipelines.

2+ years of demonstrated experience using Python, SQL, or other relevant data science programming languages.

1+ year of demonstrated experience designing predictive models.

Preferred Qualifications:
An active U.S. Government Secret level security clearance (at a minimum), to include a closed investigation date completed within the last 6 years.

Hands on experience with predictive maintenance, Prognostics and Health Management (PHM), or RCM methodologies.

Strong foundations in statistics, including survival/reliability analysis, time-series modeling, regression, and uncertainty quantification.

Strong foundation in data engineering and analysis, including structured (relational, time-series) and unstructured (logs, text, imagery) data types, data pipeline design, and hands-on experience managing real-world data quality challenges such as missingness, sensor drift, and irregular sampling.

Production experience with relevant Amazon Web Services (AWS) services (e.g., S3, Glue, SageMaker, Redshift, etc.), Palantir Foundry, Databricks, and/or Snowflake.

Proven expertise in artificial intelligence, demonstrated by hands on experience designing, building, and deploying machine learning models and AI solutions in production environments.

Experience with reliability engineering.