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About This Role
RELOCATION ASSISTANCE: No relocation assistance available
CLEARANCE REQUIRED FOR START: No
CLEARANCE TYPE: None
TRAVEL: Yes, 10% 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.
At Northrop Grumman, the Insights \& Intelligence (i2\) organization seeks to embed trusted AI and data insights into every business decision at the company. We build lightweight, production‑grade analytics solutions that solve problems traditional enterprise tools struggle to meet.
Our team operates with high autonomy, working closely with engineers and business leaders to identify high‑value problems, build apps and other products from the ground up, and deploy them into production. We value speed, intellectual curiosity, and the ability to toggle between "prototype rapidly" and "engineer for production" based on what the situation demands.
As a principal data scientist, you will lead analytical projects that drive high\-impact business decisions—working with program teams to deeply understand their challenges, developing rigorous analytical approaches and models, and delivering insights through production\-quality code, applications, and strategic recommendations.
Job duties include, but are not limited to:
- Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, formulate the right analytical questions, and translate business challenges into rigorous data science approaches
- Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need
- Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions
- Build user‑friendly, production‑grade ML/AI applications (e.g., Streamlit, Dash) and analytical artifacts that provide data insights to teams across the enterprise and enable better decision making
- Write production\-quality Python code and develop analytical pipelines using cloud\-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows
- Deliver insights through multiple channels—executive recommendations, analytical reports, interactive dashboards, and direct consultation with business leaders
- Take ownership of the technical quality and business impact of your work—make thoughtful methodological decisions, validate approaches rigorously, and stand behind your recommendations
- Stay current on analytical methods, statistical techniques, and domain\-specific best practices to continuously improve the quality and impact of your work
What Makes You Successful in this role:
You balance speed with quality: You can assess when "good enough now" beats "perfect later" and prioritize impact and working solutions over perfection.
You think deeply about problems: You don't just apply standard methods—you critically evaluate whether your analytical approach addresses the business problem, challenge your assumptions, and dig into the data to understand what's really happening.
You're intellectually curious: You get excited about jumping into new domains, asking smart questions, and learning enough about unfamiliar business problems to design the right analytical approach.
You have high agency: You proactively gather information, identify blockers, can operate in ambiguity, and make thoughtful decisions with incomplete information.
You take ownership: You're accountable for both the technical quality and business impact of your work. When you deliver a recommendation, you stand behind the methodology and can defend your approach.
You're a bridge‑builder: You can talk to engineers about implementation, business stakeholders about their problems, and executives about strategic implications. You translate and collaborate across domains.
Work Arrangement
This is a hybrid/remote position. Most of our team is based in the Northern Virginia area, and we welcome candidates who can sometimes collaborate in person, but we operate primarily remotely and value flexibility. 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.
Basic Qualifications:
- Minimum of 5 years of hands\-on experience in data science, data analysis, or other relevant professional experience
- Must have strong proficiency with Python, SQL, and Git
- Must have deep understanding of statistical methods, machine learning algorithms, and when to apply different analytical techniques
- Must have experience developing and deploying machine learning models in production environments
- Must have proven ability to translate complex business problems into rigorous analytical frameworks
- Must have demonstrated problem‑solving and critical‑thinking skills with an ability to handle complex analytical challenges
- Must have excellent communication skills and ability to deliver compelling, actionable recommendations to non‑technical stakeholders
- Must have strong track record of ownership and accountability for technical decisions and project outcomes
Preferred Qualifications:
- Experience with AWS and Databricks for data processing and model development
- Experience with PySpark for large\-scale data transformation and analytics
- Proven experience building and deploying web‑based visualization or decision‑support tools for business use cases (e.g., Streamlit, Dash)
- Knowledge of MLOps concepts and best practices for deploying models to production
- Understanding of containerization (e.g., Docker) and cloud\-based deployment
- Familiarity with advanced analytical techniques such as causal inference, experimental design, time series forecasting, optimization, or Bayesian methods
- Domain experience in program management, business management, operations research, earned value management, or financial forecasting
- Background in consulting, forward‑deployed engineering, or other client‑facing technical roles where you translated ambiguous business problems into technical solutions
CIDO
Primary Level Salary Range: $113,900\.00 \- $170,900\.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 $113K-$170K range is below the median 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 ($142K) sits 26% below the category median. Disclosed range: $113K to $170K.
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.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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