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About This Role
Company Overview: Work Where it Matters
Akima Systems Engineering (ASE), an Akima company, is not just another federal systems support contractor. As an Alaska Native Corporation (ANC), our mission and purpose extend beyond our exciting federal projects as we support our shareholder communities in Alaska.
At ASE, the work you do every day makes a difference in the lives of our 15,000 Iñupiat shareholders, a group of Alaska natives from one of the most remote and harshest environments in the United States.
For our shareholders, ASE provides support and employment opportunities and contributes to the survival of a culture that has thrived above the Arctic Circle for more than 10,000 years.
For our government customers, ASE delivers solutions in maritime IT, systems engineering, and integration across the Department of Defense and stands ready to help improve operational performance at a reasonable and sustainable cost.
As an ASE employee, you will be surrounded by a challenging, yet supportive work environment that is committed to innovation and diversity, two of our most important values. You will also have access to our comprehensive benefits and competitive pay in addition to growth opportunities and excellent retirement options.
Description:
We are seeking an experienced Senior Data Scientist to support the Program Executive Office (PEO) Digital portfolio by leading the architecture, design, and implementation of next\-generation Agentic AI capabilities for Department of Defense digital modernization initiatives. This individual will serve as the technical lead responsible for developing AI\-enabled solutions that operate seamlessly across Microsoft Azure, AWS, Google Cloud Platform (GCP), and on\-premises environments while maintaining strict security and compliance requirements for IL5 environments.
This Hybrid position requires that you live within commuting distance from North Charleston, SC.
Why Join Us
This position offers the opportunity to shape the future of Artificial Intelligence across the PEO Digital portfolio by architecting enterprise\-scale Agentic AI capabilities that support secure, multi\-cloud operations across Azure, AWS, Google Cloud Platform, and on\-premises environments. You will work alongside Government leaders, cloud architects, software engineers, cybersecurity professionals, and mission partners to deliver innovative AI solutions that accelerate digital modernization, improve mission effectiveness, and enable next\-generation decision support for the Department of the Navy. To join our team of outstanding professionals, apply today!
Responsibilities:
This role combines advanced data science, machine learning, AI orchestration, cloud architecture, and software engineering to build scalable, secure, and portable AI solutions capable of supporting mission\-critical operations across the PEO Digital portfolio and multiple computing environments.
The ideal candidate is equally comfortable discussing large language models with engineers, presenting AI architecture to senior Government leaders, and leading implementation teams through complex technical challenges.
### AI Architecture \& Strategy
- Lead the design and implementation of enterprise Agentic AI solutions supporting PEO Digital modernization initiatives.
- Design portable AI architectures capable of operating across Azure, AWS, GCP, and on\-premises environments.
- Evaluate technical feasibility of proposed AI capabilities and provide architectural recommendations.
- Develop scalable AI reference architectures that minimize vendor lock\-in while maximizing deployment flexibility.
- Recommend emerging AI technologies and best practices supporting future mission requirements.
### Agentic AI Development
Lead development of intelligent multi\-agent systems including:
- AI orchestration frameworks.
- Autonomous task planning.
- Tool execution.
- Agent collaboration.
- Workflow automation.
- Multi\-agent reasoning.
- Retrieval\-Augmented Generation (RAG).
- Enterprise knowledge management.
Experience with frameworks such as:
- Semantic Kernel.
- AutoGen.
- LangGraph.
- LangChain.
- CrewAI.
- Similar agent orchestration platforms.
### Multi\-Cloud \& Hybrid Cloud Engineering
Design and support AI deployments utilizing:
- Microsoft Azure
- Azure Arc
- Azure Kubernetes Service (AKS)
- Amazon Web Services (AWS)
- Elastic Kubernetes Service (EKS)
- Google Cloud Platform (GCP)
- Google Kubernetes Engine (GKE)
- Hybrid Cloud architectures
- Edge computing environments
- On\-premises Kubernetes deployments
- Develop cloud\-agnostic deployment strategies supporting PEO Digital enterprise modernization objectives.
### Kubernetes \& Container Platforms
Lead containerized AI deployments utilizing:
- Kubernetes.
- Azure Arc\-enabled Kubernetes.
- Docker.
- Helm.
- GitOps.
- Infrastructure as Code.
- CI/CD pipelines.
Develop highly portable AI services capable of running in multiple classified and unclassified computing environments.
### AI Model Deployment
Design and deploy production AI inference environments utilizing technologies such as:
- Hugging Face.
- vLLM.
- Text Generation Inference (TGI).
- Open\-weight Large Language Models.
- Commercial AI services where authorized.
Optimize model performance, scalability, latency, and infrastructure utilization.
### Data Science \& Machine Learning
Develop advanced analytics and machine learning solutions including:
- Predictive analytics.
- NLP.
- Document intelligence.
- Semantic search.
- Embedding generation.
- Knowledge graph integration.
- AI\-assisted decision support.
- Statistical modeling.
- Data mining.
- Feature engineering.
### Retrieval\-Augmented Generation (RAG)
Design enterprise RAG architectures utilizing:
- Vector databases.
- pgvector.
- Milvus.
- Azure Arc\-enabled PostgreSQL.
- Enterprise document repositories.
- Knowledge management systems.
Develop secure document interrogation capabilities supporting mission users.
### Security \& Compliance
Design AI systems meeting DoD security requirements including:
- Zero Trust Architecture.
- Microsoft Entra ID.
- Identity federation.
- Policy enforcement.
- Controlled Unclassified Information (CUI).
- IL5 environments.
- Audit logging.
- Data governance.
- AI governance.
Implement automated safeguards preventing ingestion or exposure of:
- Personally Identifiable Information (PII).
- Protected Health Information (PHI).
Ensure AI outputs comply with applicable security marking and release requirements.
### Technical Leadership
- Lead AI technical strategy across multiple PEO Digital programs.
- Mentor junior data scientists, ML engineers, and software developers.
- Serve as technical advisor to Program Managers and Government stakeholders.
- Present architectural recommendations to executive leadership.
- Support proposal development and technical solutioning for new business opportunities.
Qualifications:
- Bachelor's degree in computer science, Data Science, Artificial Intelligence, Engineering, Mathematics, or related technical discipline.
- Active Top Secret Clearance.
- 10\+ years of professional experience in Data Science, Machine Learning, AI, or Cloud Engineering.
- 5\+ years designing enterprise AI or ML solutions.
- Experience deploying AI solutions in cloud or hybrid\-cloud environments.
- Experience with Kubernetes and containerized applications.
- Strong experience architecting multi\-cloud AI solutions utilizing Azure, AWS, GCP, and Azure Arc.
- Experience building production machine learning pipelines.
- Strong Python programming skills.
- Experience working with REST APIs and microservices.
- Familiarity with Large Language Models and Generative AI.
- Excellent communication and technical presentation skills.
Preferred Qualifications:
- Master's or Ph.D. in AI, Machine Learning, Computer Science, Data Science, Applied Mathematics, or related discipline.
- Experience supporting Program Executive Office (PEO) Digital, NIWC Atlantic, Marine Corps Systems Command, or other Department of Defense digital modernization organizations.
- Experience with Azure Arc.
- Experience with Azure AI Foundry.
- Experience with AWS Bedrock.
- Experience with Google Vertex AI.
- Experience deploying open\-weight LLMs.
- Experience with Semantic Kernel, AutoGen, LangGraph, or similar orchestration frameworks.
- Experience implementing Retrieval\-Augmented Generation (RAG).
- Experience with vector databases.
- Experience supporting Department of Defense customers.
- Experience supporting IL5 or classified computing environments.
- Active Secret Clearance or higher.
Preferred Certifications:
- Microsoft Certified: Azure AI Engineer Associate.
- Microsoft Certified: Azure Solutions Architect Expert.
- AWS Certified Machine Learning – Specialty.
- Google Professional Machine Learning Engineer.
- Certified Kubernetes Administrator (CKA).
- Certified Kubernetes Application Developer (CKAD).
- Security\+.
- PMP (preferred).
Job ID: 2026\-24504 Work Type: Hybrid
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 Akima, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Akima AI Hiring
Akima has 1 open AI role right now. They're hiring across Data Scientist. Based in North Charleston, SC, US.
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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