Senior Data Scientist

General Dynamics Information Technology•USA FL MacDill AFB - 7115 S Boundary Blvd (FLC096), FL

5d•$153,000 - $207,000•Onsite

About The Position

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. The ideal candidate brings deep experience in model development across multiple problem domains—forecasting, NLP, anomaly detection, and classification—and can independently lead the ML practice for the team.

Requirements

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.
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

Nice To Haves

Experience with model optimization and conversion (ONNX, TensorRT, OpenVINO) for edge or tactical deployment
Knowledge of NLP techniques applied to defense or intelligence domains (entity extraction, document classification, summarization of operational reports)
Familiarity with distributed data frameworks (Apache Spark, Dask)
Experience with edge AI hardware (NVIDIA Jetson, Coral TPU)

Responsibilities

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
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
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
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

Benefits

Comprehensive benefits and wellness packages
401K with company match
competitive pay
paid time off
AI-powered career tool that identifies career steps and learning opportunities
An internal mobility team focused on helping you achieve your career goals
variety of medical plan options, some with Health Savings Accounts
dental plan options
a vision plan
a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match.
full flex work weeks where possible
a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave.
short and long-term disability benefits
life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available.