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About This Role
RELOCATION ASSISTANCE: Relocation assistance may be available
CLEARANCE REQUIRED FOR START: Yes
CLEARANCE TYPE: Top Secret
TRAVEL: Yes, 25% of the TimeDescription
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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.
Northrop Grumman is seeking an experienced Principal or Senior Principal Data Scientist to join our team of qualified, diverse individuals in Melbourne, Fl. This position is required full\-time, in\-office. There is no option for remote or hybrid work arrangements.
Accomplish
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We are seeking a first\-principles thinker and skilled data scientist to solve complex data challenges. This is not a role for operating pre\-built tools; this is an opportunity to architect and build the core data logic of our systems. You will be responsible for creating custom, high\-performance statistics and machine learning tools from the ground up, making meaning of complex data sets. Your work will involve deep data modeling, abstracting reusable patterns, and writing modular, maintainable code that stands the test of time.
Essential Functions:
- Collaborate closely with software engineers, analysts, and other data scientists to understand data requirements and deliver robust, integrated solutions.
- Architect and develop custom, maintainable data science solutions from the ground up.
- Write and maintain tests and documentation for data architecture, workflows, and processes.
- Champion software engineering best practices within the data domain, including comprehensive unit/integration testing, CI/CD automation (Git, Docker), and robust documentation.
- Tune and optimize complex algorithms within our on\-premise infrastructure.
Succeed
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The individual we seek will be self\-motivated, proactive, goal\-oriented to help us grow our services, become even better at what we do and will possess the following qualifications.
\*\*This requisition may be filled at either a Principal Level or a Sr. Principal Level\*\*
Basic Qualifications for a Principal Data Scientist:
- Bachelor of Science degree in Data Science, Computer Science, Software Engineering, Applied Mathematics, or related field, with a minimum of 5 years of relevant experience, OR Master of Science degree in relevant fields with 3 years of relevant experience.
- Deep understanding of Machine Learning and Deep Learning
- Recent professional experience in software development and/or data engineering.
- Strong programming skills in Python and SQL.
- Familiarity with CI/CD pipelines and version control (e.g., Git, Docker, etc.)
- Your ability to transfer and maintain the final adjudicated government Top Secret clearance, and any program access (es) required for the position within a reasonable period of time, as determined by the company. Active In Scope Top Secret Clearance required to start.
Basic Qualifications for a Sr Principal Data Scientist:
- Bachelor of Science degree in Data Science, Computer Science, Software Engineering, Applied Mathematics, or related field, with a minimum of 8 years of relevant experience, OR Master of Science degree in relevant fields with 6 years of relevant experience, OR PhD/Doctorate degree in relevant fields with 4 years of relevant experience.
- Deep understanding of Machine Learning and Deep Learning
- Recent professional experience in software development and/or data engineering.
- Strong programming skills in Python and SQL.
- Familiarity with CI/CD pipelines and version control (e.g., Git, Docker, etc.)
- Your ability to transfer and maintain the final adjudicated government Top Secret clearance, and any program access (es) required for the position within a reasonable period of time, as determined by the company. Active In Scope Top Secret Clearance required to start.
Preferred Qualifications:
- Master’s degree or higher, in Data Science or related discipline.
- 7 years of experience in software development.
- Experience with C/C\+\+.
- Experience with machine learning pipelines and data science workflows.
- Familiarity with SQL Alchemy, Numpy, Pandas, SKLearn, PyTorch.
- Ability to troubleshoot and optimize complex data queries and workflows.
- Familiarity with big data concepts, machine learning pipelines, and MLOps.
- Excellent problem\-solving skills with the ability to troubleshoot and optimize across the entire data stack.
- Ability to work in a fast\-paced environment.
Thrive with Us
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At Northrop Grumman we are invested in the growth and well\-being off all our employees. We offer flexible work arrangements, phenomenal learning opportunities, exposure to a wide variety of projects and customers, and a very friendly team environment.
Our Total Rewards Program offers a comprehensive employee benefits package, including a Retirement and Savings Plan, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts.
We have Employee Resource Groups (ERGs) that provide benefits for the member, our leaders and the company. Our ERGs offer opportunities to be a friend, be active, be a volunteer, be a leader, to be recognized and to be yourself. Every ERG is inclusive of all employees!
If you are ready to join us in defining possible, apply now.
Primary Level Salary Range: $108,200\.00 \- $162,400\.00
Secondary Level Salary Range: $135,000\.00 \- $202,600\.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 $108K-$202K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 22% below the category median. Disclosed range: $108K to $202K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Northrop Grumman AI Hiring
Northrop Grumman has 3 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist. Positions span Dulles, VA, US, San Diego, CA, US, Melbourne, FL, US. Compensation range: $187K - $258K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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