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 #RDG Target Salary Range $229,000 - $366,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.

• Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform • Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform • Collaborate with product managers and engineers to define product requirements and translate them into data science solutions • Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition • Communicate findings and recommendations to stakeholders across the organization • Stay up-to-date on the latest advancements in machine learning and data science • Mentor and guide junior data scientists on the team

• Help build the long term product strategy by identifying opportunities to attract new users, increase engagement, and drive retention • Influence strategic roadmaps through data-driven insights into user behaviors and needs • Design metrics that help evaluate the health of the business and the success of our products, including any ETL development needed for consistent and robust analysis • Drive experimentation from design through execution and analysis to maximize learnings and guide future investment decisions • Build self-serve tools for product and engineering partners that answer common questions and/or increase data literacy in the organization • Work cross functionally with product, engineering, and design teams to ensure insights make it to the product • Scale your work to other parts of the data organization through mentoring more junior data scientists, improving processes, and providing perspective on the most important problems

• Develop action-oriented insights to drive the product strategy through observational causal analysis and experiment meta-analysis, and clearly communicate results to stakeholders up to the C-suite to take action based on the recommendations • Uplevel experimentation practices on the team through guiding design, execution, and deep dive analyses to maximize learnings from A/B tests • Create new ETLs, tables, dashboards, and other self-serve tools to enable other data scientists and cross-functional partners to find and interact with data seamlessly • Design, evaluate, and/or measure team-level KPIs to enable quarterly goal setting and demonstrate team impact • Regularly engage with stakeholders to gather feedback and share progress on work at all stages to ensure alignment between DS and other teams on business goals and outcomes • Mentor more junior data scientists and business partners in data science best practices and methods to increase data literacy and improve decision making