Responsibilities
Peraton is seeking an experience Lead Data Scientist to join our team of qualified and diverse individuals. This position supports our 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.
As a Lead Data Scientist at Peraton, 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.
Location: Suitland, MD (Hybrid)
Day to Day Roles and 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