AI/ML SME

Goldbelt, Inc.•Arlington, VA

21h

About The Position

A leader in cutting-edge research and technology in the cyber arena, CPMG focuses on using business drivers to guide cybersecurity activities and manage risk. CPMG offers flexible, integrative solutions for Department of Defense (DoD) contractors, among others, and specializes in cybersecurity, information and operational technology, electronic security surveillance, and support services. The AI/ML SME will be the Analytic team lead.

Requirements

Strong problem-solving skills and ability to plan and deliver technical solutions independently.
Excellent written and verbal communication skills.
Ability to manage multiple priorities and deliver under tight deadlines.
Strong analytical skills and attention to detail.
5+ years in-depth knowledge of at least one analytical / statistical language (for example: Python, R, and SQL).
At least 2 years of custom Machine Learning and AI model training (to include supervised and unsupervised) and Natural Language Processing (NLP) expertise focusing on unstructured data.
3+ years’ experience with SQL database / coding.
2+ years’ experience with data visualization tools.
2+ years’ experience with the ability to manipulate unstructured data.
Must have a Secret clearance.

Nice To Haves

Educational background in Data Science, is a plus.
Bachelor’s degree in a related field.

Responsibilities

Evaluate and assess DEA enterprise data holdings identified as possible data sources.
Develop AI/ML models/techniques to enrich DEA’s data to achieve advantages towards DEA mission.
Review/analyze and propose different data scientist techniques and implementations that can be considered and implemented to advance DEA’s data enrichment processes.
Once any model/technique is identified, trained, and fully tested, the team shall coordinate and facilitate the implementation and maintenance of each model/technique, adjusting and improving as the DEA enterprise data holdings expand.
Design and implement machine learning or AI models aligned to approved business use cases.
Select algorithms and training approaches consistent with data type and performance objectives.
Conduct iterative testing and tuning to achieve required accuracy and performance metrics.
Document model assumptions, data inputs, and training parameters for reproducibility.
Conduct independent validation of model results for accuracy, stability, and bias detection.
Package and deploy the validated model to the designated test or production environment.
Configure application interfaces, APIs, or automation pipelines required for model operation.
Conduct functional and security testing to confirm stable integration.
Establish model monitoring and retraining mechanisms to detect performance drift.
Develop and deliver comprehensive technical documentation detailing data sources, model design, validation results, and deployment configuration.
Prepare AI business case summaries that explain model purposes, value proposition, expected outcomes, and return-on- investment (ROI) for leadership decision making.
Create briefing materials and presentation decks suitable to upper management or executive review.
Highlighting business relevance, benefits, and potential integration opportunities.
Design and implement ETL processes to extract, clean, and standardize source data for model training.
Data ingestion successful; no errors; data accuracy ≥ 95%; walkthrough completed and accepted by Gov PM.