Principal Data Scientist – Factory Intelligence

$107K - $204K Tucson, AZ, US Senior Data Scientist

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Skills & Technologies

AwsAzureMlflowPythonPytorchTensorflow

About This Role

AI job market dashboard showing open roles by category

Date Posted:

2026\-07\-09Country:

United States of America

Location:

US\-AZ\-TUCSON\-M09 \~ 3350 E Hemisphere Loop \~ BLDG M09

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's 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.

As a Principal Data Scientist – Factory Intelligence, you will play a key role in transforming factory data into actionable intelligence that directly impacts production performance. Working across Engineering, Operations, and Quality, you will lead the development and deployment of predictive analytics solutions that improve yield, reduce variation, and drive smarter decision\-making at scale.

This is a hybrid role based in Tucson, Arizona.

What You Will Do:

  • Transform factory data into actionable intelligence that improves production performance.
  • Collaborate with Engineering, Operations, and Quality teams to build and deploy predictive analytics solutions.
  • Develop models that directly impact yield, reduce variation, and support smarter decision\-making.
  • Design, deploy, and maintain production\-grade machine learning solutions.
  • Build intuitive data visualization tools and statistical analysis applications.
  • Partner with stakeholders to translate complex data into clear, practical insights.
  • Provide technical leadership and mentor junior data scientists and engineers.
  • Establish best practices in applied data science across the organization.
  • Become a subject matter expert in factory test data and uncover opportunities for improvement.
  • Solve challenging, data\-driven manufacturing problems and deliver measurable production enhancements.
  • Work directly with customers to ensure data is fully leveraged to improve performance.
  • Contribute to scalable, production\-ready data science solutions and help advance the organization’s analytics standards.
  • Operate effectively in a fast\-paced, multi\-tasking environment.

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 a minimum of 5 years of experience
  • Experience developing in Python (NumPy, SciPy, scikit\-learn, scikit\-image) for production\-grade statistical or machine learning applications (beyond academic examples)
  • Demonstrated experience deploying, maintaining, and scaling machine learning models in production environments
  • Experience with relational database management and SQL development
  • U.S. Citizen \- 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

Qualifications We Prefer:

  • Experience with statistical tools such as Minitab, R, JMP, or SAS
  • Strong knowledge of machine learning pipelines and MLOps practices (e.g., MLflow), including versioning, monitoring, and lifecycle management
  • Strong experience applying quantitative techniques (normalization, standardization, applied statistics) to analyze large, complex datasets and build deployable machine learning models, including in cloud environments such as AWS or Azure
  • Experience designing, training, fine\-tuning, and deploying deep learning models using frameworks such as PyTorch or TensorFlow
  • Experience working with large language models (LLMs), including fine\-tuning, evaluation, and deployment; or demonstrated deep knowledge of LLM concepts and architectures
  • Experience operating in a technical leadership or mentoring

Learn More \& Apply Now

Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:

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.*

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Salary Context

This $107K-$204K range is above 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

Company Raytheon
Title Principal Data Scientist – Factory Intelligence
Location Tucson, AZ, US
Category Data Scientist
Experience Senior
Salary $107K - $204K
Remote No

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 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

Aws (30% of roles) Azure (24% of roles) Mlflow (4% of roles) Python (51% of roles) Pytorch (15% of roles) Tensorflow (11% of roles)

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 ($156K) sits 19% below the category median. Disclosed range: $107K to $204K.

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.

Raytheon AI Hiring

Raytheon has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span McKinney, TX, US, Tucson, AZ, US. Compensation range: $204K - $298K.

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

Based on 463 roles with disclosed compensation, the median salary for Data Scientist positions is $192,890. Actual compensation varies by seniority, location, and company stage.
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.
About 14% of the 3,708 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Raytheon is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Data Scientist positions include Senior Data Scientist, ML Engineer, AI Product Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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