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Develop and deploy predictive models, machine learning algorithms, and advanced analytics for Department of Defense missions. Responsibilities include data exploration, feature engineering, statistical analysis, model deployment and monitoring in secure environments, analytical visualization, stakeholder collaboration, and mentoring junior data scientists. The role requires an active TS/SCI clearance and experience with Python or R, AI/ML techniques, data governance, and federal compliance.

Own end-to-end delivery of data, analytics, and AI-enabled products for the Joint Staff CDAO. Translate mission needs into product vision, roadmaps, user stories, acceptance criteria, and prioritized backlogs. Coordinate stakeholders, data scientists, engineers, analysts, and cybersecurity teams; manage iterative delivery, risks, dependencies, reviews, and executive communications. Monitor performance and user satisfaction while supporting governance, strategic planning, and continuous improvement for Department of Defense initiatives.

Design, implement, and maintain software and MLOps capabilities for a counter-UAS program. Lead development of services, integrate and productionize ML models, implement end-to-end MLOps pipelines (training, versioning, deployment, monitoring), debug mission-critical systems, and mentor teammates while ensuring security, reliability, and compliance for DoD/defense environments.

Rapidly prototype offensive cyber capabilities and exploit/CNO tools, embed with mission teams, analyze proof-of-concept exploits, collaborate with vulnerability researchers, mentor teammates, and produce developer/tester documentation for mission-critical cyber operations.

Lead design, development, integration, and testing of AI/ML-enabled, cloud-native C-UAS software. Architect distributed, real-time systems and AI/ML pipelines, mentor teams, ensure secure, maintainable code, and support test, evaluation, and lifecycle activities for mission-critical defense solutions.

Perform traffic engineering analyses and microsimulation modeling (VISSIM), prepare technical reports, calibrate/validate models, support traffic studies, and develop engineering deliverables. Engineer I completes assignments and uses CAE/CAD as applicable; Engineer II independently develops products, provides technical direction, and coordinates multidisciplinary teams. Hybrid NYC role with occasional field work.

Design and implement data science workflows and data mesh pipelines to support C-UAS detection, tracking, classification, and decision-support. Develop ML/statistical models for anomaly and threat assessment, build cloud/hybrid data platforms and interoperable pipelines, enforce data tagging and governance, create visualizations and dashboards, mentor junior staff, and ensure mission-ready, secure analytics for real-time operations.

Design and implement multi-language software for a C-UAS program, build distributed data fabric and data fusion pipelines, evaluate technologies, debug mission-critical systems, mentor developers, and collaborate with systems, data, and test teams to validate solutions in lab and field environments.

Design, implement, and lead development of mission-critical C-UAS software across multiple languages. Instrument and interpret metrics/telemetry for observability, diagnose complex faults, recommend technology trade-offs, mentor developers, and collaborate with systems and field teams to ensure robust, secure, and maintainable solutions.

Lead regional business development for transportation infrastructure in the East Central U.S., growing market share through client relationships, strategic teaming, pipeline development, capture leadership, proposal management, CRM tracking, and industry engagement to win work across delivery models.