Lead Data Scientist

Peraton

. Define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program .

Posted 9/24/2026full-timeSuitland • Maryland • United StatesSenior💰 $146,000 - $234,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and driving enterprise data science and AI/ML strategies, with a strong focus on MLOps implementation, advanced analytics solutions, and AI governance frameworks. Proven ability to lead cross-functional teams and integrate AI/ML capabilities into complex systems while ensuring compliance and operational reliability.

Highest-signal resume keywords

MLOps ImplementationMachine Learning ExpertiseNLP and LLMsAI Governance FrameworksDataOps and Agile Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingAdvanced AnalyticsDeep LearningReinforcement LearningAnomaly DetectionTime Series AnalysisPythonRSpark

Soft Skills

Technical LeadershipCollaborationMentoringCommunicationProblem-Solving

Tools & Technologies

TensorFlowPyTorchDatabricksCI/CD IntegrationEvent-Driven ArchitecturesStreaming Analytics

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

AI/ML StrategyEnterprise ModernizationData QualityComplianceFederal Programs

Tech Stack

Tools & technologies

CloudPythonPyTorchSparkTensorflow

About the role

Key responsibilities & impact

Define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program
Provide technical leadership across enterprise data science and AI/ML initiatives
Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production
Lead MLOps implementation for model lifecycle management, automation, observability, and continuous improvement
Apply 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 subject matter experts to integrate AI/ML capabilities into enterprise systems and workflows
Support system-of-systems integrations across multiple systems, vendors, contractors, and interdependent platforms
Establish AI governance frameworks for 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
Improve 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 and promote organizational best practices

Requirements

What you’ll need

Bachelor's degree and 16 years of experience, or an Associate's degree and 18 years of experience, or a High School diploma/equivalent and 20 years of experience
Must be a U.S. Citizen
Ability to obtain a Public Trust clearance
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
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 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
PhD highly preferred in a related technical field
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

Benefits

Comp & perks

Potential eligibility for overtime
Shift differential
Discretionary bonus
Equal opportunity employer, including disability and protected veterans protections