Interested in this Data Scientist role at Raytheon?
Apply Now →Skills & Technologies
About This Role
Date Posted:
2026-09-24Country:
United States of America Location:
US-AZ-TUCSON-863A ~ 1151 E Hermans Rd ~ 863A Position Role Type:
Hybrid U.S. Citizen, U.S. Person, or Immigration Status Requirements:
Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type:
DoD Clearance: Secret Security Clearance Status:
Ability to obtain INTERIM U.S. government issued security clearance is required prior to start date At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.
Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today’s mission and stay ahead of tomorrow’s threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense.
Raytheon’s Data, Analytics, and AI organization is seeking a hands-on Senior Data Scientist to help turn complex factory and test data into insights and solutions that improve how we build products. In this role, you will work closely with Engineering, Operations, Quality, and software teams to solve real manufacturing problems using statistics, machine learning, and data-driven applications. Your work will have a direct connection to production, from identifying the causes of process variation to developing predictive models and tools that help engineers make faster, better decisions.
You’ll engage across the full data science lifecycle: exploring large, multifaceted datasets, developing and validating models alongside domain experts, and helping transition successful solutions into production environments. You will also play a key role in guiding how our organization applies modern analytics and AI techniques within manufacturing.
This is a role for someone who enjoys both the technical work and the problem-solving that happens when data meets the real world.
What You Will Do
- Develop statistical and machine learning solutions that address manufacturing, test, quality, and production challenges
- Explore large and complex datasets to identify patterns, process drivers, anomalies, and opportunities for improvement
- Build and deploy Python-based analytical and machine learning applications that are used by engineers and factory teams
- Partner directly with engineers, operators, and technical stakeholders to understand problems and translate them into practical data solutions
- Develop visualizations and analytical tools that make complex data easier to understand and act on
- Help establish sustainable approaches for deploying, monitoring, and maintaining machine learning solutions
- Mentor other data scientists and engineers and contribute to the team's technical direction and best practices
- Build deep expertise in factory and test data and use that knowledge to identify new opportunities for data science and automation
- Your work will not stop at a notebook or prototype. Successful solutions will be used to influence real engineering and manufacturing decisions.
This is a hybrid role based in Tucson, Arizona.
Qualifications You Must Have
- Typically requires a University degree or equivalent experience and a minimum of 8 years of prior relevant experience or an Advanced Degree in a related field and minimum 5 years experience.
- Professional experience using Python for data analysis, statistical modeling, or machine learning
- Experience developing machine learning or advanced analytical solutions beyond academic or experimental projects
- Strong understanding of applied statistics and quantitative analysis
- Experience working with relational databases and SQL
- Ability to communicate technical findings and recommendations to both technical and non-technical stakeholders
- Experience analyzing manufacturing, engineering, test, quality, or other complex technical datasets
- U.S. Citizen - The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance.
Qualifications We Prefer
- You do not need to have experience in all of the areas below. Experience in one or more would be valuable:
- Experience deploying or supporting machine learning models in production environments
- Experience with Python libraries such as NumPy, SciPy, pandas, scikit-learn, or scikit-image
- Experience with statistical analysis tools such as Minitab, R, JMP, or SAS
- Knowledge of machine learning lifecycle and MLOps practices, including model versioning, monitoring, evaluation, and tools such as MLflow
- Experience with cloud-based data or machine learning environments such as AWS or Azure
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Experience applying large language models or generative AI to practical business or engineering problems
- Experience mentoring other technical professionals or providing technical leadership
What We Offer
Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation.
Learn More & Apply Now!
Please consider the following role type definition as you apply for this role.
Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.
As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote.
The salary range for this role is 107,500 USD - 204,500 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.
Privacy Policy and Terms:
Salary Context
This $107K-$204K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Raytheon, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($156K) sits 18% below the category median. Disclosed range: $107K to $204K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Raytheon AI Hiring
Raytheon has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Tucson, AZ, US, Richardson, TX. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.