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About This Role
This position may be filled prior to the posted deadline. Interested candidates are encouraged to apply as soon as possible.
Koniag Emerging Technologies (KET), a Koniag Government Services company, is seeking an experienced AI Data Scientist with a Secret security clearance to support KET and our government customer. The position is remote, but in the DC metro area is preferred.
Benefits include medical, dental, and vision insurance, 401(k) retirement plan, paid time off, paid parental leave, life and disability insurance, flexible spending accounts, commuter benefits and tuition reimbursement.
The ideal candidate is a data-driven problem solver who is passionate about leveraging artificial intelligence and machine learning tools to transform raw usage data into actionable business insights. This individual will play a key role in modernizing how application performance and usage metrics are tracked, analyzed, and communicated across the organization, including for executive-level reporting.
The AI Data Scientist will be responsible for tracking, analyzing, and reporting on usage statistics across 15+ web applications, while introducing and implementing AI-driven tools and methodologies to enhance data analysis capabilities.
Principal responsibilities will include but are not limited to:
- Design and implement systems to track and monitor usage statistics across 15+ enterprise web applications.
- Analyze application usage data to identify trends, patterns, anomalies, and opportunities for optimization.
- Research, evaluate, and introduce AI and machine learning tools to automate and enhance data analysis processes.
- Develop predictive models and analytics frameworks to forecast application usage trends and user behavior.
- Create executive-level reports and dashboards summarizing key metrics, insights, and recommendations for leadership.
- Collaborate with application owners, IT teams, and business stakeholders to understand data needs and reporting requirements.
- Design and maintain data pipelines to ensure accurate, timely, and reliable collection of usage data.
- Translate complex data findings into clear, actionable insights for both technical and non-technical audiences.
- Present analytical findings and strategic recommendations to executive leadership on a regular basis.
- Stay current with emerging AI/ML technologies and recommend innovative approaches to improve data analytics capabilities.
- Ensure data integrity, security, and compliance with organizational and government data handling policies.
- Document methodologies, models, and processes for reproducibility and knowledge transfer.
Required Education and Experience:
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field from an accredited college or university.
- 5+ years of experience in data science, data analytics, or a related field.
- Demonstrated experience tracking and analyzing usage statistics for multiple software/web applications.
- Experience introducing or implementing AI/ML tools within an organizational analytics environment.
- Experience creating executive-level reports and presentations based on data analysis.
Required Skills and Competencies:
- Exceptional communication skills in English – both written and oral – and the ability to translate complex technical data into clear insights for executive audiences.
- Strong proficiency in data analysis tools and programming languages such as Python, R, or SQL.
- Experience with AI/ML frameworks and tools.
- Proven ability to design and build dashboards and visualizations using tools such as Power BI, Tableau, or similar platforms.
- Strong understanding of statistical analysis, predictive modeling, and data mining techniques.
- Experience working with large datasets from multiple application sources and consolidating them into unified reporting structures.
- Ability to work independently and collaboratively within a team environment under the direction of leadership.
- Strong analytical and problem-solving skills with keen attention to detail.
- Ability to manage multiple projects and priorities in a fast-paced environment.
- Must have secret clearance.
Desired Skills and Competencies:
- Experience working in a federal government IT environment.
- Familiarity with cloud-based analytics platforms (e.g., AWS, Azure, or Google Cloud AI/ML services).
- Experience with natural language processing (NLP) tools for generating automated reporting narratives.
- Knowledge of application performance monitoring (APM) tools and their integration with analytics platforms.
- Experience presenting data-driven recommendations directly to C-level executives or senior government stakeholders.
- Familiarity with Agile methodologies and working within cross-functional technical teams.
Our Equal Employment Opportunity Policy
The company is an equal opportunity employer. The company shall not discriminate against any employee or applicant because of race, color, religion, creed, ethnicity, sex, sexual orientation, gender or gender identity (except where gender is a bona fide occupational qualification), national origin or ancestry, age, disability, citizenship, military/veteran status, marital status, genetic information or any other characteristic protected by applicable federal, state, or local law. We are committed to equal employment opportunity in all decisions related to employment, promotion, wages, benefits, and all other privileges, terms, and conditions of employment.
The company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or apply for a position on our website, please get in touch with Heaven Wood via e-mail at [email protected] or by calling 703-488-9377 to request accommodations.
Koniag Government Services (KGS) is an Alaska Native Owned corporation supporting the values and traditions of our native communities through an agile employee and corporate culture that delivers Enterprise Solutions, Professional Services and Operational Management to Federal Government Agencies. As a wholly owned subsidiary of Koniag, we apply our proven commercial solutions to a deep knowledge of Defense and Civilian missions to provide forward leaning technical, professional, and operational solutions. KGS enables successful mission outcomes for our customers through solution-oriented business partnerships and a commitment to exceptional service delivery. We ensure long-term success with a continuous improvement approach while balancing the collective interests of our customers, employees, and native communities. For more information, please visit www.koniag-gs.com.
Equal Opportunity Employer/Veterans/Disabled. Shareholder Preference in accordance with Public Law 88-352
Salary Context
This $135K-$175K range is below the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Koniag Government Services, 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 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 18% below the category median. Disclosed range: $135K to $175K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Koniag Government Services AI Hiring
Koniag Government Services has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $175K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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