Lead Data Scientist

Lead Data Scientist

This position supports Revolutional's federal customer as part of an application transformation and modernization initiative.

This 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 DevSecOps and scaled Agile practices across the organization.

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

Position Description

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.

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

Technical Environment

• Cloud-native AI/ML and analytics environments (AWS, Azure)
• Distributed data platforms and enterprise analytics ecosystems
• Python, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworks
• MLOps pipelines, model deployment platforms, and automation frameworks
• Real-time streaming and event-driven analytics systems
• DevSecOps pipelines and CI/CD automation practices
• DataOps and Agile/SAFe delivery environments
• APIs, distributed integrations, and enterprise data platforms
• Collaboration and delivery tools (Git, Jira, Confluence)
• High-volume, near real-time processing environments

What You Bring (Requirements)

Baseline 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

Technical Capabilities

• 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

Core Strengths

• 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 Have (Differentiators)

• 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