Senior Data Scientist

About the role

We are seeking a Senior Data Scientist to design, train, evaluate, and deliver machine learning models that solve operational problems across USCENTCOM’s Data Office initiatives. This is a hands-on ML practitioner role—not a platform or infrastructure position. The Senior Data Scientist will work within an established on-premises Data Analytical Environment (DAE) built on a Data Lakehouse architecture with H100 GPU infrastructure, applying their expertise in statistical modeling, deep learning, and applied ML to turn enterprise data into actionable intelligence.

Responsibilities

• Model Development & Training
• Design, train, and validate supervised, unsupervised, and deep learning models using open-source libraries (PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM) to support forecasting, classification, anomaly detection, and NLP use cases
• Conduct rigorous experiment design: feature engineering, hyperparameter tuning, cross-validation, and evaluation using appropriate metrics (precision/recall/F1, RMSE, AUC-ROC) to ensure production-quality model performance
• Fine-tune and adapt open-source LLMs (LLMA, Mistral, and similar) for domain-specific tasks including document summarization, entity extraction, and question-answering over classified and unclassified networks
• Develop and maintain RAG pipelines: chunking strategies, embedding model selection, retrieval evaluation, and prompt engineering to deliver high-quality LLM-augmented analytics
• Applied Problem-Solving
• Translate mission requirements into ML solutions: work directly with analysts, operators, and leadership to scope problems, define success criteria, and deliver models that produce actionable operational insights
• Build models across multiple domains including predictive analytics (logistics, readiness), NLP/text analytics (reports, intelligence documents), anomaly detection (cybersecurity, network, behavioral), and computer vision where applicable
• Design lightweight, optimized models for edge and disconnected environments when required, supporting model optimization and conversion (ONNX, TensorRT, OpenVINO) for tactical deployment
• MLOps & Lifecycle (Collaborative)
• Version, track, and reproduce experiments using MLflow, DVC, and Git; maintain clear documentation of model lineage, training data, and performance baselines
• Package trained models for deployment in containerized environments (Docker, Kubernetes) in coordination with the platform engineering team. Ownership of deployment infrastructure is flexible and project-dependent
• Integrate models into existing CI/CD pipelines, analytics platforms, and decision support tools in collaboration with the DevSecOps and data engineering teams

Requirements

• Data Security & Compliance
• Ensure all model development adheres to DoD security, encryption, and data handling standards, including tagging, metadata management, and retention policies
• Operate within classified environments (SIPR/NIPR), following cybersecurity and data stewardship protocols across air-gapped and hybrid infrastructure

Qualifications

• Education & Experience
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, Data Science, or related quantitative field
• 8+ years of hands-on AI/ML model development experience with a strong record of delivering production models, not just prototypes
• Compliant with DoD Directive 8140 (i.e., CompTIA Security + CE cert)
• Active Secret clearance is required. Must be TS/SCI eligible
• Must be able to work on site at MacDill AFB. Not a remote role
• Technical Skills
• Strong Python proficiency and deep experience with open-source ML frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, Hugging Face Transformers)
• Demonstrated ability to train, fine-tune, and evaluate models end-to-end—from raw data through feature engineering, model selection, training, validation, and production handoff
• Experience with LLM fine-tuning techniques (LoRA, QLoRA, PEFT) and RAG architecture design (vector databases, embedding strategies, retrieval evaluation)
• Working knowledge of MLOps toolchains (MLflow, DVC, Weights & Biases) and version control (Git)
• Familiarity with containerized deployment (Docker, Kubernetes) in air-gapped or on-premise environments
• Experience working with large-scale data systems and medallion/lakehouse architectures

Skills

• AI Frameworks
• Applied Problem Solving
• Data Compliance
• Machine Learning Model Development

Benefits

At GDIT, the mission is our purpose, and our people are at the center of everything we do. Growth: AI-powered career tool that identifies career steps and learning opportunitiesSupport: An internal mobility team focused on helping you achieve your career goalsRewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time offCommunity: Award-winning culture of innovation and a military-friendly workplace

Pay

The likely salary range for this position is $153,000 - $207,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Schedule

Scheduled Weekly Hours: 40

Telecommuting Options

Telecommuting Options: Onsite Work Location: USA FL MacDill AFB