Lead Data Scientist

Peraton•Suitland, MD

6d•$146,000 - $234,000•Hybrid

About The Position

Peraton is seeking an experienced Lead Data Scientist to join their team. This role supports a customer's application transformation and modernization initiative, focusing on a large-scale transformation of systems into a data-centric, cloud-native ecosystem. The program involves modernizing legacy applications, developing new cloud-native solutions, and implementing DevSecOps and scaled Agile practices. The Lead Data Scientist will define and drive enterprise data science and AI/ML strategy, leading efforts in advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration. This position requires collaboration with various teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows. The role operates at both strategic and hands-on levels, guiding technical direction, developing models, and ensuring analytics solutions deliver measurable mission impact.

Requirements

Bachelors degree and 16 years of experience or an Associates degree and 18 years of experience or a High School diploma/equivalent and 20 years of experience.
Must be a U.S. Citizen with the 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 (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.

Nice To Haves

PhD is highly preferred in 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.

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.