Data Scientist 3 - 29243

HII's Mission Technologies division•Suffolk, VA

3d•$103,467 - $145,000•Hybrid

About The Position

Mission Technologies, a division of Huntington Ingalls Industries, is seeking an experienced Data Scientist to support the Joint Training Synthetic Environment (JTSE) and JLVC Modernization effort under the Joint Staff J7 (JS J-7) contract. This role focuses on advancing data-driven capabilities within joint training, simulation, and synthetic environments. Leadership at HII is a mindset, not a title. Through our Leadership Capability Framework, we define how every employee grows, leads, and contributes—regardless of role. It sets the standard for how you can develop yourself and what you can expect from leaders across our organization. We look for candidates who want to grow in alignment with these capabilities: Know & Grow Your People – Commit to learning and supporting team success. Build Relationships – Communicate openly, collaborate well, and build trust. Take Ownership – Deliver on commitments and take pride in your work. Customer First – Focus on the mission and those we serve. Shape the Future – Bring ideas, curiosity, and continuous improvement. Act with Urgency – Take initiative and follow through with purpose. These capabilities guide how all employees contribute to our shared success across Mission Technologies.

Requirements

5 years of related experience with a Bachelor’s degree; 3 years with a Master’s; 0 years with a PhD/JD; or 9 years of relevant experience with a high school diploma
Hands-on experience building and deploying machine learning or statistical models in operational environments
Strong knowledge of data processing, architectures, and distributed systems
Proficiency in Python or R and common data science libraries
Experience with data wrangling, feature engineering, and exploratory data analysis
Understanding of supervised/unsupervised learning and model evaluation
Experience creating technical documentation
Ability to work in cross-functional teams and manage multiple priorities
Strong analytical and problem solving skills
Experience working in Linux environments
Must have an active Secret Clearance

Nice To Haves

Experience developing or fine-tuning large-scale or foundation models (LLMs, multimodal models).
Familiarity with modern AI/ML frameworks.
Experience deploying ML models into production systems or enterprise environments.
Knowledge of real-time data processing and streaming technologies (Kafka).
Familiarity with Joint Training and Simulation environments such as JTSE, JLVC, or similar DoD systems.
Understanding of data governance, data standards, and enterprise data strategy.
Experience with simulation data or synthetic environments.
Familiarity with MLOps/DevSecOps pipelines and secure model deployment.
Experience supporting joint, multi-domain, or coalition operations.
Relevant certifications (AWS, Kubernetes, Security+, CISSP, or similar).

Responsibilities

Lead end-to-end data science workflows, including problem definition, data acquisition, feature engineering, model development, deployment, and lifecycle monitoring
Translate operational and training objectives into data-driven approaches and define analytical requirements for AI/ML integration into JTSE and Joint Training Tools
Design and maintain scalable data pipelines and architectures for real-time and near-real-time ingestion, processing, and analysis
Develop, train, and optimize machine learning models—including large-scale and foundation models—to enhance simulation and decision-support capabilities
Ensure data interoperability, quality, and consistency across multiple sources and systems
Integrate AI/ML models into simulation and synthetic environments, including FISE, to enable advanced analytics and scenario generation
Support exercise planning, execution, and after-action analysis using predictive analytics, anomaly detection, and performance metrics
Define and enforce data standards, schemas, and governance practices
Conduct model testing, validation, and performance evaluations
Identify and mitigate risks related to data quality, model bias, scalability, and performance
Support MLOps/DevSecOps practices for secure, reproducible, and continuous delivery
Collaborate with cross-functional government, industry, and mission partners
Document data pipelines, models, methodologies, and analytical findings for technical and non-technical audiences