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About This Role
Data Scientist / AI Engineer
Location: Norfolk, Virginia
Employment Type: Full\-time, On\-site
Security Clearance: Active NATO or U.S. National SECRET clearance required
Citizenship: Must be a citizen of a NATO member nation
The Role
Ironclad is seeking an experienced Data Scientist / AI Engineer to support the development and implementation of advanced data science, artificial intelligence, and large language model capabilities within the NATO enterprise.
This position requires a technically versatile professional who can bridge data engineering, software development, machine learning, and operational mission requirements. The selected candidate will design scalable data architectures, build and optimize data pipelines, develop API\-based infrastructure, and support the secure deployment of AI and machine learning solutions in cloud\-based and hybrid environments.
The role requires strong hands\-on experience with generative AI, large language models (LLMs), distributed systems, microservices, containerized applications, and modern software engineering practices. The successful candidate must also be able to translate complex operational challenges into practical technical solutions for military and civilian stakeholders.
Key Responsibilities* Develop and implement scalable data science and AI capabilities supporting NATO initiatives.
- Design, build, and maintain data pipelines for structured and unstructured data.
- Prepare, cleanse, transform, and optimize data for LLM training, fine\-tuning, inference, and analytics.
- Develop API\-based infrastructure that integrates LLMs and machine learning models with operational systems.
- Design and support microservices and containerized AI/ML applications.
- Build distributed data storage and processing solutions using cloud\-based or hybrid architectures.
- Develop real\-time data processing and streaming capabilities for operational decision support.
- Automate data engineering processes and improve the scalability, efficiency, and reliability of AI infrastructure.
- Implement monitoring, logging, traceability, and performance\-optimization tools for data pipelines and APIs.
- Support the secure deployment of AI and LLM solutions in Microsoft Azure, AWS, or comparable environments.
- Develop tools that improve data accessibility for data scientists, analysts, engineers, and operational users.
- Collaborate with data scientists, software engineers, system architects, and other technical stakeholders.
- Support federated learning, cross\-domain data sharing, and secure collaboration across NATO nations.
- Develop proofs of concept for LLM\-based and advanced analytics applications.
- Evaluate operational requirements and recommend appropriate AI, software, and data\-engineering solutions.
- Create dashboards, reports, and visual analytics for senior and non\-technical stakeholders.
- Provide technical briefings, mentoring, and training in AI engineering, data science, API development, and digital literacy.
- Research emerging developments in generative AI, distributed computing, data architecture, and software engineering.
- Promote responsible, secure, and ethical AI practices throughout solution development and deployment.
Required Qualifications* Bachelor’s degree or higher from a nationally recognized university in data science, data analytics, artificial intelligence, mathematics, physics, computer science, software engineering, or a closely related discipline.
- At least four years of professional experience as a Data Scientist, Machine Learning Engineer, Data Engineer, Software Engineer, or in a closely related role.
- Demonstrated experience developing operational AI or machine learning solutions.
- Experience with distributed systems and cloud\-based or hybrid architectures.
- Experience designing API\-based infrastructure and microservices architectures.
- Hands\-on experience developing and deploying containerized applications using technologies such as Docker or Kubernetes.
- Demonstrated experience with generative AI and large language models.
- Experience preprocessing data and supporting the fine\-tuning and deployment of LLMs in secure, scalable environments.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit\-learn, or comparable technologies.
- Strong programming experience with Python, Java, Scala, or similar languages.
- Experience with version control, CI/CD pipelines, automated testing, and modern software engineering practices.
- Experience building and optimizing ETL processes, data pipelines, and real\-time streaming solutions.
- Familiarity with Apache Airflow, Kafka, Spark, or comparable data\-engineering technologies.
- Experience architecting or maintaining data lakes, data warehouses, distributed storage systems, or NoSQL solutions.
- Knowledge of platforms such as Delta Lake, Snowflake, Hadoop, or comparable technologies.
- Experience applying AI to operational decision support and the analysis of unstructured data, including text or imagery.
- Strong understanding of data security, privacy, sovereignty, and responsible AI practices.
- Experience developing dashboards, visual reports, and analytics using Tableau, Microsoft Power BI, Kibana, or comparable tools.
- Ability to translate operational problems into practical AI and machine learning solutions.
- Demonstrated success working with multidisciplinary technical teams.
- Strong written and verbal communication skills.
- Ability to explain technical concepts to non\-technical stakeholders and senior leaders.
- Ability to mentor or train personnel in AI engineering, data science, or software development concepts.
Preferred Qualifications* Familiarity with NATO processes, organizational structures, operational culture, and decision\-making procedures.
- Experience supporting military, defense, government, or international organizations.
- Experience developing AI or data\-engineering solutions using open\-source frameworks and publicly available datasets.
- Familiarity with military staff workflows and operational planning processes.
- Experience with federated learning and privacy\-preserving collaboration across multiple organizations or nations.
- Experience supporting cross\-domain data sharing and API\-driven interoperability.
- Familiarity with agile project\-management methods and tools such as JIRA, Trello, or Microsoft Loop.
- Experience briefing senior leaders and presenting actionable, data\-driven recommendations.
- Knowledge of ethical AI principles, including bias mitigation, responsible data handling, transparency, and secure deployment.
Why Join Ironclad
Ironclad supports complex defense and international missions by providing experienced professionals who combine technical expertise with an understanding of operational requirements. This position offers the opportunity to contribute directly to secure, scalable, and mission\-focused AI capabilities while working alongside military, civilian, and technical stakeholders across the NATO enterprise.
Clearance
This position requires an active NATO or National SECRET (or higher) security clearance. Applicants who do not possess the clearance specified above cannot be considered at this time.
Compensation
Compensation for this position ranges from $115,000 \- $130,000 annually. Final salary will be based on factors such as experience, education, skills, qualifications, contract requirements, and overall affordability.
Eligible full\-time employees may also receive a comprehensive benefits package, including medical and dental insurance, retirement benefits, paid leave, and professional development opportunities.
How to Apply
Email your resume to [email protected] with the subject line: Data Scientist / AI Engineer
- (Your Name) Application" or respond to this job posting via the included web application.
Ironclad Defense Works is an Equal Opportunity Employer.
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Salary Context
This $115K-$130K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Ironclad Defense Works, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($122K) sits 36% below the category median. Disclosed range: $115K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ironclad Defense Works AI Hiring
Ironclad Defense Works has 1 open AI role right now. They're hiring across Data Scientist. Based in Norfolk, VA, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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