Director, Data Science
Responsibilities
Peraton's Risk Decision Group is seeking a Director of Information / Data Science to lead the RDG Data Science as a Service (DSaaS) initiative, driving the organization's transition from proof-of-concept to production-scale AI and analytics services. This role owns the strategy and execution of a scalable, cloud-native intelligence platform designed to embed AI-driven insight across all RDG operations — spanning model development and deployment, GenAI, agentic workflows, and intelligent document processing. The ideal candidate combines deep technical expertise in ML Ops and production AI systems with the cross-functional leadership needed to navigate hybrid cloud architecture, compliance, and enterprise data governance in a government contracting environment.
This is a 100% Remote position.
Responsibilities
- Lead the DSaaS initiative through phased production rollout, from initial cloud-based use cases through full hybrid cloud integration and enterprise-wide AI service delivery.
- Own and evolve the DSaaS service architecture across platform, service, feature, and user tiers — ensuring scalable, repeatable, and compliant delivery of intelligence services across RDG operations.
- Drive the ML Ops strategy, establishing standardized pipelines for model training, evaluation, deployment, and monitoring across a cloud-native, GovCloud-compliant environment.
- Support and co‑lead the data access and governance strategy in close partnership with enterprise data and cybersecurity teams, ensuring alignment across derivative, organizational, and authoritative data tiers with compliance and ATO requirements.
- Develop and deploy production-quality AI/ML models — including risk scoring, anomaly detection, intelligent document processing, and LLM/agentic solutions — that drive measurable operational improvements.
- Represent the data science team in cross-functional coordination with Cyber, Infrastructure, Compliance, and Engineering stakeholders to align on hybrid cloud architecture and enterprise data strategy.
- Champion a "Buy Over Build" philosophy — evaluating and integrating commercial platforms where possible, reserving custom development for requirements that commercial solutions cannot meet.
- Build and manage a lean, high-performing data science team; identify and advocate for dedicated engineering support in cloud infrastructure, DevOps, and platform administration required to scale.
- Evaluate emerging AI/ML technologies and identify strategic opportunities to continuously expand DSaaS capabilities and service offerings.
Qualifications
Required Qualifications
- US Citizenship required
- Must be able to obtain and maintain a Top Secret eligible clearance; active federal background investigation with a T5 adjudication is preferred
- 16 years general work experience
- 10+ years of combined experience in AI/ML, data science, data engineering, and production platform delivery
- Demonstrated experience leading AI/ML systems from proof of concept to production at scale, including model deployment, monitoring, and retraining pipelines
- Expertise in ML Ops platforms and practices, including experiment tracking, model registries, and CI/CD for data science workflows
- Strong experience with cloud-native data and ML platforms in secure or regulated environments
- Proven ability to lead cross-functional initiatives involving infrastructure, cybersecurity, compliance, and data governance stakeholders
- Experience working with data engineering and governance teams to design and deploy scalable solutions involving data categorization, environment-to-environment data movement, and governance practices.
- Strong leadership and communication skills, with the ability to represent technical strategy to executive leadership and drive alignment across organizational boundaries
Preferred Qualifications
- Bachelor's Degree in Data Science, Computer Science, or a related field
- Hands-on experience with ML Ops platforms supporting GenAI, LLM fine-tuning, agentic workflows, and intelligent document processing
- Familiarity with ATO processes and hybrid cloud compliance in a government contracting environment
- Experience with background investigation operations or federal data programs
- Experience with COTS evaluation and procurement in a government or regulated environment
- Knowledge of DevOps automation for data science deployments and cloud platform administration
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