AI Data Scientist (REMOTE)

Koniag Government Services, LLC•Washington, DC

1d•$135,000 - $175,000•Remote

About The Position

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 its government customer. The position is remote, with preference for candidates in the DC metro area. Applications are accepted through 12/13/26. The role focuses on tracking, analyzing, and reporting usage statistics across more than 15 web applications, and introducing AI-driven tools and methodologies to improve data analysis. The successful candidate will use application usage data to generate actionable business insights and modernize how application performance and usage metrics are tracked, analyzed, and communicated, including through executive-level reporting.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field from an accredited college or university.
At least 5 years of experience in data science, data analytics, or a related field.
Demonstrated experience tracking and analyzing usage statistics for multiple software or 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.
Exceptional written and oral English communication skills, including 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 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 attention to detail.
Ability to manage multiple projects and priorities in a fast-paced environment.
Must have a Secret clearance.

Nice To Haves

Experience working in a federal government IT environment.
Familiarity with cloud-based analytics platforms, such as 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.

Responsibilities

Design and implement systems to track and monitor usage statistics across more than 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 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 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.