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About This Role
The Kansas City National Security Campus (KCNSC), managed and operated by Honeywell Federal Manufacturing \& Technologies, is a premier advanced manufacturing facility that supports the safety, security, reliability and effectiveness of our nation's nuclear deterrent. With nearly 7,000 employees combined in Kansas City, Missouri, and Albuquerque, New Mexico, KCNSC protects our nation and allies by producing trusted national security products and services for the U.S. Department of Energy's National Nuclear Security Administration.
We create innovative solutions to complex national security challenges, and lead with accountability, collaboration and an unwavering dedication to our mission and core values.
Are you ready to do work that matters?
Summary
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The Sr Data Scientist \- Applied AI role leads moderately complex end\-to\-end design, development, operationalization, and ongoing support of advanced artificial intelligence solutions for manufacturing or business applications, ensuring models are scalable, reliable, and seamlessly integrated into frontend and backend systems.
Duties and Responsibilities
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- Lead the development of AI/ML models and algorithms using disciplined software‑engineering practices, including version control, automated testing, and thorough documentation.
- Lead teams to design, build, and maintain production‑grade pipelines for model training, validation, deployment, and monitoring, applying CI/CD and containerization principles.
- Architect and expose model inference services via robust APIs, integrating them with stakeholder applications and user interfaces.
- Optimize model performance for latency, throughput, and resource utilization to meet real‑time manufacturing or business requirements.
- Collaborate with multiple stakeholders to translate business needs into AI system specifications, define service‑level objectives, and provide technical guidance to data‑science partners.
- Mentor and support continuous improvement of other engineers, scientists, and interns.
You Must Have
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- At least five years relevant experience in data science or related technical activities.
- BS in engineering from ABET accredited institution or Bachelor of Science degree in data science or related field, OR two additional years of relevant experience.
- Ability to travel as determined by the needs of the business.
- Ability to work remote, hybrid, or on\-site as directed by management and is determined by the needs of the business.
- Regular and reliable attendance is an essential function of this job.
- United States Citizenship.
- Ability to obtain and maintain, if required for position, a U.S. Department of Energy (DOE) security clearance (some positions require additional DOE designations).
We Value
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- Experience leading a technical team to deliver business or manufacturing focused solutions.
- Ability to grasp complex technical and business problems, prioritize tasks, and devise innovative, production‑ready AI solutions that align with manufacturing or enterprise objectives.
- Expert‑level proficiency in a modern programming language (e.g., JavaScript, Python, Java, C\+\+) and strong command of database/query languages (e.g., SQL) for building scalable data pipelines and services.
- Demonstrated software‑engineering discipline: version control, unit/integration testing, continuous integration‑continuous deployment (CI/CD), and documentation of code and system architecture.
- Strong communication skills (verbal, written, presentation) to convey technical concepts to stakeholders, collaborate with cross‑functional teams, and produce clear technical specifications and run‑books.
- Deep knowledge of machine‑learning and deep‑learning concepts, including model design, training, evaluation, and optimization for performance, latency, and
- Familiarity with distributed data‑processing frameworks and big‑data ecosystems (e.g., Spark, Kafka) to support high‑throughput training and real‑time inference pipelines.
- Proven ability to create actionable visualizations or dashboards that show model performance, data quality, and operational metrics for end‑users and decision‑makers.
- Hands\-on experience deploying AI applications on cloud platforms such as Azure or AWS.
- Expert understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval\-Augmented Generation (RAG).
*This job description/job posting is not all inclusive and other duties may be assigned.*
What We Offer
We are focused on attracting, hiring and retaining talented people who power our mission. Our robust total rewards package recognizes performance, fuels development and supports flexibility. Key features include:
- Medical, dental and vision insurance
- Health Savings Account (HSA)
- Industry leading 401(k) match
- Generous paid time off
- Flexible work schedule
- Tuition Reimbursement
- Professional Certification \& License Programs
- Mission driven culture
Click here to learn more about our benefits and culture.
Connect with us on social media: Facebook, LinkedIn and X.
Additional Information:
- Job ID: 5032
- Category: Engineering
- Level of Experience: Experienced Professional
- Posting Location: KCNSC North 9221 Ward Parkway Kansas City, MO, 64114
- Remote Eligibility: On\-Site
- Travel Required: 0\-10%
- Approved Work States: MO; KS
- Hourly/Salary: Salary
- Division: 800
- Department: E65
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 Kansas City National Security Campus, 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.
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.
Kansas City National Security Campus AI Hiring
Kansas City National Security Campus has 2 open AI roles right now. They're hiring across Data Scientist. Based in Kansas City, MO, US.
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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