Lead Data Scientist

Lead Data ScientistThis 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 DescriptionAs 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.ResponsibilitiesProvide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization programDesign, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environmentsLead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvementApply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysisDesign and support event-driven analytics and real-time/streaming ML pipelinesCollaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflowsSupport system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platformsEstablish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management FrameworkDevelop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverablesSupport DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model deliveryEnsure analytics solutions align with enterprise security, privacy, and compliance requirementsDrive improvements in data quality, validation, accessibility, and operational analytics reliabilityPresent findings, recommendations, and technical approaches to executive leadership and stakeholdersMentor data scientists and analytics teams while promoting best practices across the organizationTechnical EnvironmentCloud-native AI/ML and analytics environments (AWS, Azure)Distributed data platforms and enterprise analytics ecosystemsPython, R, Spark, TensorFlow, PyTorch, Databricks, and related ML frameworksMLOps pipelines, model deployment platforms, and automation frameworksReal-time streaming and event-driven analytics systemsDevSecOps pipelines and CI/CD automation practicesDataOps and Agile/SAFe delivery environmentsAPIs, distributed integrations, and enterprise data platformsCollaboration and delivery tools (Git, Jira, Confluence)High-volume, near real-time processing environmentsWhat You Bring (Requirements)Baseline Requirements U.S. Citizenship with the ability to obtain a Public TrustPhD in Data Science, Computer Science, Statistics, Mathematics, or related field15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiativesProven experience leading AI/ML and analytics efforts across large-scale, complex systemsAbility to obtain and maintain a Public Trust clearanceTechnical Capabilities Deep expertise in machine learning, statistical modeling, and advanced analytics techniquesExperience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scalingExperience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analyticsExperience designing and supporting real-time or streaming analytics architecturesExperience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platformsExperience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigationExperience with large-scale distributed data environments and cloud-native analytics platformsExperience with DataOps, CI/CD integration, and Agile/SAFe delivery modelsStrong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologiesExperience developing dashboards, technical reports, analysis plans, and reproducible analytics workflowsExperience collaborating across engineering, architecture, application, and operational teams at enterprise scaleCore Strengths Strong ownership mindset with accountability for enterprise AI/ML outcomesAbility to translate complex analytical findings into actionable business and mission insightsStrategic thinker capable of balancing innovation, governance, and operational realitiesEffective communication across technical, operational, and executive stakeholdersStrong leadership and mentoring capabilities across data science and analytics teamsAbility to operate in complex, fast-moving, multi-contractor delivery environmentsNice to Have (Differentiators)Experience supporting statistical and similarly large-scale federal modernization programsExperience implementing enterprise AI governance or responsible AI initiativesExperience with event-driven architectures, streaming analytics, or operational AI systemsExperience supporting large-scale data modernization or enterprise analytics transformation effortsExperience working within DevSecOps-enabled AI/ML delivery environments