Lead Data Scientist

Overview

Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes. We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy.

Lead Data Scientist

Location: Suitland, MD (Hybrid)

Terms: Full-time

Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required

Travel: %

This position supports Revolutional's federal customer as part of an application transformation and modernization initiative. The program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of Dev Sec Ops  and scaled Agile practices across the organization.

The core challenge is orchestrating complex, multi-contractor delivery while transforming both technology and operating models without disrupting mission-critical operations.

As a Lead Data Scientist at Revolutional, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program. You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows.

You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact.

• Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program
• Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments
• Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement
• Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis
• Design and support event-driven analytics and real-time/streaming ML pipelines
• Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows
• Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms
• Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework
• Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables
• Support Data Ops and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery
• Ensure analytics solutions align with enterprise security, privacy, and compliance requirements
• Drive improvements in data quality, validation, accessibility, and operational analytics reliability
• Present findings, recommendations, and technical approaches to executive leadership and stakeholders
• Mentor data scientists and analytics teams while promoting best practices across the organization

• Cloud-native AI/ML and analytics environments (AWS, Azure)
• Distributed data platforms and enterprise analytics ecosystems
• Python, R, Spark, Tensor Flow, PyTorch, Databricks, and related ML frameworks
• MLOps pipelines,…