AI Engineer (Hybrid)

DAn Solutions•Charlottesville, VA

2d•Hybrid

About The Position

DAn Solutions is seeking a Senior Data Scientist / Data Engineer / AI Engineer to support advanced data analytics, data engineering, artificial intelligence, and machine learning capabilities for a federal intelligence customer. This position will work with real-world mission data to develop analytics, predictive models, data pipelines, visualizations, and AI/ML capabilities that integrate into production software environments.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
3+ years of Data Scientist experience supporting software development, analytics, or mission-focused systems.
Experience applying statistical modeling, machine learning, and data-mining techniques to real-world datasets.
Proficiency in Python, R, or Scala.
Experience building data models and analytics that integrate into production software systems.
Experience supporting the DoD, Intelligence Community, or other federal programs.
Familiarity with visualization tools such as Tableau or Power BI.
Strong SQL background with experience using PostgreSQL and Elasticsearch.
Working knowledge of cloud-based data environments.
Active TS/SCI clearance.

Nice To Haves

Certification in machine learning, data analytics, or cloud platforms.
IAT Level II or III certification.

Responsibilities

Conduct data analysis, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis.
Apply scientific and analytical techniques to large and complex datasets.
Develop written, visual, graphical, and verbal data products.
Design and implement data storage solutions using relational and non-relational databases.
Develop scripts to cleanse, transform, and prepare data for operational use.
Create AI/ML-based tools to improve analysis and automate processes.
Integrate AI capabilities into software-development applications.
Develop and evaluate data models and analytical capabilities for production systems.
Assess and communicate risks associated with AI/ML implementations.
Support data visualization and reporting.