Lead Data Scientist

Revolutional, LLC•McLean, VA

55d•$150,000 - $210,000•Hybrid

About The Position

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.

Requirements

U.S. Citizenship with the ability to obtain a Public Trust
PhD in Data Science, Computer Science, Statistics, Mathematics, or related field
15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives
Proven experience leading AI/ML and analytics efforts across large-scale, complex systems
Ability to obtain and maintain a Public Trust clearance
Deep expertise in machine learning, statistical modeling, and advanced analytics techniques
Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling
Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics
Experience designing and supporting real-time or streaming analytics architectures
Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms
Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation
Experience with large-scale distributed data environments and cloud-native analytics platforms
Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models
Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies
Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows
Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale
Strong ownership mindset with accountability for enterprise AI/ML outcomes
Ability to translate complex analytical findings into actionable business and mission insights
Strategic thinker capable of balancing innovation, governance, and operational realities
Effective communication across technical, operational, and executive stakeholders
Strong leadership and mentoring capabilities across data science and analytics teams
Ability to operate in complex, fast-moving, multi-contractor delivery environments

Nice To Haves

Experience supporting statistical and similarly large-scale federal modernization programs
Experience implementing enterprise AI governance or responsible AI initiatives
Experience with event-driven architectures, streaming analytics, or operational AI systems
Experience supporting large-scale data modernization or enterprise analytics transformation efforts
Experience working within DevSecOps-enabled AI/ML delivery environments

Responsibilities

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 DataOps 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