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About This Role
RELOCATION ASSISTANCE: No relocation assistance available
CLEARANCE REQUIRED FOR START: No
CLEARANCE TYPE: Secret
TRAVEL: NoDescription
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At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history \- from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.
Description
Join Northrop Grumman on our continued mission to push the boundaries of possible across land, sea, air, space, and cyberspace. Enjoy a culture where your voice is valued and start contributing to our team of passionate professionals providing real\-life solutions to our world’s biggest challenges. We take pride in creating purposeful work and allowing our employees to grow and achieve their goals every day by Defining Possible. With our competitive pay and comprehensive benefits, we have the right opportunities to fit your life and launch your career today.
The Defense Systems (DS) Sector Digital Enablement (DE) Organization in the Strategic Deterrence Systems (SDS) Division is seeking a Data Scientist/Principal Data Scientist, this is a dual level position, located in Roy, UT to support the Condition\-Based Maintenance Plus (CBM\+) team. This position supports the effort to modernize the ground\-based leg of the United States’ nuclear triad, the Sentinel Weapon System (WS), using CBM\+, which is the Department of Defense’s (DoD) preferred method of maintenance and sustainment for large\-scale weapon systems. CBM\+ works to optimize scheduled maintenance, thereby reducing Lifecycle Cost (LCC).
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
- T02: 2 Years with Bachelors in Science; 0 Years with Masters
- T03: 5 Years with Bachelors in Science; 3 Years with Masters; 1 Year with PhDData Scientist: Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, Mechanical/Aerospace Engineering, or a related STEM field and 3 years of experience; OR a Master’s degree and 1 year of experience.
Principal Data Scientist: Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, Mechanical/Aerospace Engineering, or a related STEM field and 5 years of experience; OR a Master’s degree and 3 years of experience; OR a PhD and 1 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.
Primary Level Salary Range: $87,200\.00 \- $130,800\.00
Secondary Level Salary Range: $108,200\.00 \- $162,400\.00
The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions.
Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business.
The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.
Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit http://www.northropgrumman.com/EEO. U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.
Salary Context
This $87K-$162K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Northrop Grumman, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($124K) sits 35% below the category median. Disclosed range: $87K to $162K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Northrop Grumman AI Hiring
Northrop Grumman has 5 open AI roles right now. They're hiring across Data Scientist, Research Scientist, AI Software Engineer. Positions span Remote, US, Corinne, UT, US, Dulles, VA, US. Compensation range: $147K - $206K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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