Chief Data Officer

Chenega Corporation•Bath Township, OH

9d•Onsite

About The Position

The Chief Data Officer shall provide advanced technical support to the AFRL/RA organization, focusing on the development and implementation of data-driven solutions to enhance research and engineering outcomes. This role supports the Chief Data Officer and AI leadership in designing and implementing digital engineering infrastructure, leveraging artificial intelligence (AI), machine learning (ML), and data science methodologies to transform large volumes of experimental, computational, and historical research data into actionable insights. The position plays a critical role in modernizing how hypersonic test and technical report data is accessed, structured, analyzed, and utilized—enabling improved research efficiency and innovation across AFRL initiatives.

Requirements

Master of Arts (MA)/Master of Science (MS) in Science; Engineering, Mathematics, or Statistics
Minimum ten (10) years in data science / OR A-type work
Minimum five (5) experience in designing AI/ML solutions, building data architectures, and creating automation (AI agents)
Minimum five (5) years working with large-scale or complex datasets
Minimum five (5) years supporting DoD research programs
Possess and maintain an Active Secret Clearance
Knowledge of machine learning, artificial intelligence, and data science techniques
Ability to design and implement data architectures and workflows aligned with enterprise/government standards
Proficiency in handling structured and unstructured data, including data extraction and transformation
Skilled in integrating heterogeneous data sources into unified systems
Ability to develop and support AI-driven automation tools (e.g., data extraction agents)
Possess analytical and problem-solving skills applied to complex research datasets
Possess effective collaboration skills across technical, engineering, and research stakeholders
Possess excellent written and verbal communication skills for technical audiences
Ability to translate complex data into meaningful insights for decision-makers
Demonstrated technical communication skills, including preparation of briefings, technical reports, research papers, and publications.

Nice To Haves

Experience with high-speed flight vehicle technologies, hypersonics, or advanced aerospace systems
Experience with digital engineering concepts and frameworks
Experience working with large, complex, and diverse datasets
Experience in AI/ML model development, training, or deployment
Familiarity with generative AI tools and data-driven infrastructure development

Responsibilities

Support the AFRL/RA team in the collection, storage, management, and retrieval of experimental and computational research data.
Develop and implement data-driven solutions to enhance research and engineering outcomes
Support the Chief Data Officer and AI leadership
Design and implement digital engineering infrastructure, leveraging artificial intelligence (AI), machine learning (ML), and data science methodologies to transform large volumes of experimental, computational, and historical research data into actionable insights
Assist in implementing and advancing digital engineering infrastructure aligned with government reference architectures.
Apply machine learning and AI-powered data mining techniques to: Identify statistical trends within existing datasets, Detect gaps in available data (data paucity), Improve the effectiveness of future experimental and computational efforts
Collaborate with AFRL/RA AI leadership to design and develop AI agents capable of: Automating extraction of structured data from unstructured historical data packages, Utilizing metadata and technical reports to prioritize relevant information for end users
Develop and maintain scalable data structures and workflows that enable seamless: Data integration, Analysis, Storage and retrieval across multiple platforms
Integrate heterogeneous datasets from multiple sources into unified analytical frameworks and modeling environments.
Support the creation of digital collaboration tools to: Eliminate data silos, Enhance accessibility of structured research data, Provide streamlined user interfaces for data interaction, Enable transformation of fragmented information into structured, accessible, and analysis-ready datasets.
Contribute to model development and data-driven decision support tools for research stakeholders.
Other duties as assigned