Data Analytics and AI Integrator

PENDING CONTRACT AWARD WINTER 2026

Overview

Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With more than 1,700+ team members, 1,500+ AI & data experts, and 100+ prime contracts, we deliver on a scale and with purpose.

We’ve been recognized as a Top Workplace by The Washington Post for six consecutive years and named to the Inc. 5000 list of Fastest-Growing Private Companies in 13 of the past 14 years. Credence is a welcoming environment for those looking to grow and make a meaningful impact. We encourage employees to push boundaries and help solve critical, world-changing federal challenges.

Position Summary

Credence has an upcoming need for a Data Analytics and AI Integrator serves as the senior contractor authority for data analytics, automation, artificial intelligence, and machine learning initiatives across the SAF/CN support program. This position will Lead integration across cybersecurity, compliance and policy, business enterprise management, enterprise IT, and program-management activities. This position will establish a coordinated delivery approach, align initiatives with Government data and responsible-AI standards, and ensure that analytics and AI solutions are secure, explainable, reusable, measurable, and ready for Government decision or operational adoption.

Primary Responsibilities

Enterprise Leadership, Governance, and Integration

• Lead and integrate contractor Data and AI execution across SAF/CNZ, SAF/CNB, SAF/CNS, and program-management activities, coordinating priorities with the applicable division leads and the Contractor Program Manager.
• Serve as the principal contractor interface for cross-cutting Data and AI matters and coordinate with SAF/CND, SAF/CTO, system owners, Authorizing Officials, portfolio managers, infrastructure teams, and other stakeholders to align with enterprise standards and avoid duplicative or conflicting solutions.
• Establish and maintain an integrated Data and AI roadmap, use-case inventory, prioritization process, dependency map, and delivery backlog that connect initiatives to mission outcomes, performance requirements, authoritative data sources, and accountable owners.
• Lead cross-functional teams of data analysts, data engineers, data architects, cybersecurity specialists, cloud architects, automation developers, and mission subject matter experts; set priorities, resolve dependencies, review quality, and elevate delivery, security, data, or adoption risks.
• Brief senior DAF leaders on Data and AI opportunities, portfolio status, measurable benefits, resource implications, risk, adoption barriers, and recommended decisions.

Data Analytics, Engineering, and Decision Support

• Design and oversee analytics products that integrate authoritative cybersecurity, compliance, portfolio, architecture, asset, financial, operational, and infrastructure data to provide timely, decision-quality insight.
• Establish repeatable practices for data discovery, profiling, validation, cleansing, reconciliation, metadata management, lineage, quality measurement, access-control mapping, and authoritative-source designation.
• Guide development of reusable data models, taxonomies, data dictionaries, data pipelines, application programming interfaces, dashboards, scorecards, and automated workflows using Government-approved platforms.
• Apply advanced analytics to identify trends, anomalies, systemic control failures, aging actions, cost and capacity drivers, modernization candidates, and mission-risk priorities, with qualified human validation before Government use.
• Translate analytic findings into clear executive briefings, decision papers, implementation recommendations, and measurable performance indicators.

AI/ML Strategy, Delivery, and Responsible Use

• Identify and assess AI/ML opportunities across cyber compliance and auditing, eMASS and workflow automation, policy comparison, business-process optimization, portfolio and investment analysis, forecasting, architecture metadata quality, asset and license management, and enterprise IT modernization.
• Develop business cases and implementation recommendations addressing mission value, data readiness, security, privacy, records management, technical maturity, interoperability, scalability, cost, workforce impact, and acquisition or licensing implications.
• Lead approved pilots and implementations through data preparation, model or service selection, evaluation-criteria definition, testing, human-in-the-loop control design, deployment planning, user adoption, and post-deployment monitoring.
• Establish and maintain model inventories, registrations, model cards, provenance records, evaluation and bias-assessment results, human-oversight controls, and monitoring for accuracy degradation and model drift.
• Ensure all AI-assisted output is reviewed by qualified personnel and that Government data is used only with approved services, models, environments, classifications, and handling controls.

AI Security, Authorization, and Data Protection

• Integrate security and authorization considerations from inception, including AI/ML system architecture and data-flow analysis, authorization boundaries, control allocation, inherited controls, evidence requirements, and residual-risk documentation.
• Address training and inference data integrity, provenance, model versioning, adversarial inputs, prompt injection, output validation, endpoint access, secrets management, supply-chain risk, and post-deployment monitoring.
• Apply data-level protection through classification-aware tagging and handling, encryption, least privilege, attribute-based access control, privacy safeguards, records-management controls, and Zero Trust principles.
• Assess reuse of existing Government-licensed analytics, automation, cloud, and AI platforms before recommending new acquisition, and avoid unnecessary vendor lock-in or proprietary dependencies.