Position Summary
Prometheus Federal Services (PFS) is a trusted partner of federal health agencies, seeking a Machine Learning Engineer/Data Scientist to support advanced analytics initiatives within the Department of Veterans Affairs. This position will drive innovation in predictive analytics, machine learning (ML), and artificial intelligence (AI) to improve operational decision-making and health outcomes across the Veterans Health Administration. Additionally, this position will support PFS in scaling tools and expanding knowledge of AI within corporate applications.

Essential Duties and Responsibilities

Drive the development and deployment of cutting-edge data science solutions to address complex organizational challenges
Collaborate with multi-disciplinary teams, shaping strategic initiatives that innovate and optimize business operations through advanced analytics, predictive modeling, and automation
Perform advanced statistical analyses and data modeling to extract actionable insights from large, complex federal healthcare datasets
Design, develop, and deploy AI/ML models for predictive analytics, natural language processing, and decision support systems
Design, develop, and deploy robust machine learning and deep learning models for a variety of data-driven use cases
Develop reproducible workflows, documentation, and scalable pipelines for data ingestion, feature engineering, model training, validation, and deployment
Continuously research and apply state-of-the-art AI/ML methods to improve accuracy, interpretability, and scalability of deployed models
Drive knowledge sharing by introducing new tools, frameworks, and methodologies reflective of AI/ML best practices
Support client stakeholders by presenting findings in clear, compelling visualizations and narratives for executive audiences

Minimum Qualification

Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field
Minimum five (5) years of experience applying machine learning or AI techniques to solve real-world problems, with a focus on deploying production-grade solutions
Demonstrated expertise with Python (preferred) or R, including building and packaging reusable code, modules, and libraries
Hands-on experience with ML libraries such as scikit-learn, TensorFlow, or PyTorch, and a strong understanding of model lifecycle management (training, tuning, deployment, monitoring)
Demonstrated experience implementing production-grade machine learning solutions and deploying at scale (on-premise or on cloud platforms such as AWS, Azure, or GCP)
Experience implementing end-to-end Python projects and tooling, including automation scripts, data pipelines, and reproducible workflows
Proven ability to design, develop, and document RESTful APIs and integrate with major APIs (e.g., OpenAI, Azure Cognitive Services, Hugging Face)
Familiarity with version control (Git/GitHub), CI/CD pipelines, and DevOps/MLOps best practices
Strong understanding of statistical modeling, supervised/unsupervised learning, and model evaluation metrics
Must have strong typing and computer skills
Must be able to read, understand, speak, and write English fluently
Authorized to work in the U.S. indefinitely without sponsorship
Ability to obtain a public trust

Preferred Qualification

Ten (10) years of experience applying machine learning and AI techniques to solve real-world problems
Experience supporting the Department of Veterans Affairs or other federal health systems
Familiarity with VHA data sources (CDW, VSSC, IPEC) and healthcare-related metrics
Knowledge of MLOps best practices, including CI/CD for ML pipelines and modeling monitoring
Experience communicating technical results to senior government leaders and non-technical stakeholders

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