Data Analytics and AI Integrator

Credence•Washington, DC

8h•Hybrid

About The Position

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. 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.

Requirements

Active TS/SCI security clearance.
Bachelor's degree or equivalent combination of education, professional training, and directly relevant experience in data science, analytics, computer science, information systems, engineering, cybersecurity, mathematics, operations research, or a related discipline.
At least 12 years of progressively responsible experience in data analytics, data engineering or architecture, AI/ML, automation, enterprise IT, cybersecurity analytics, digital modernization, or a closely related field.
Demonstrated experience leading multidisciplinary technical teams and coordinating enterprise initiatives across multiple organizations or mission areas in a complex Federal, DoW/DoD, DAF, or comparably regulated environment.
Demonstrated ability to take Data or AI initiatives from use-case definition and data-readiness assessment through design, pilot, evaluation, implementation, governance, and performance monitoring.
Working knowledge of responsible-AI, data governance, cybersecurity, privacy, records management, cloud, and Zero Trust principles sufficient to integrate specialist inputs and advise senior leaders.
Experience developing or overseeing executive dashboards, analytical models, automated workflows, data pipelines, technical assessments, strategies, roadmaps, governance artifacts, and decision-quality briefings.
Ability to work in a hybrid NCR environment, report to the Pentagon or other Government facilities within required response times, and perform independently without routine Government or Contract PM assistance.

Nice To Haves

Graduate degree in data science, analytics, artificial intelligence, computer science, engineering, cybersecurity, information systems, operations research, or a related discipline.
Prior experience supporting SAF/CN, Headquarters Air Force, a Service CIO/CDAO, DoW/DoD CIO, or an equivalent enterprise Data and AI governance organization.
Project Management Professional (PMP), Certified Data Management Professional (CDMP), AWS or Azure data/cloud certification, security certification, or a recognized AI/ML credential.
Experience with NIST AI RMF, AI system authorization, model risk management, MLOps, data catalogs, business-intelligence platforms, robotic process automation, and Government-approved generative-AI environments.
Demonstrated experience producing measurable cycle-time, rework, cost, data-quality, risk-reduction, or decision-speed improvements through analytics, automation, or AI.

Responsibilities

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.
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.
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.
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.
Own the technical quality, integration, security, and timely delivery of cross-program Data and AI products and maintain a Government-visible roadmap, use-case inventory, dependency register, and benefits tracker.
Ensure every recommended or implemented AI use case includes documented mission value, authoritative data sources, data-readiness and risk assessment, responsible-AI controls, human-review requirements, evaluation criteria, and an accountable Government decision owner.
Maintain at least 95% accuracy and timeliness for analytics, dashboards, automation products, and decision support, correcting identified quality issues promptly and documenting material data limitations.
Prevent the introduction of Government data into unapproved AI services or tools and ensure analytics and AI artifacts remain auditable, transferable, and supportable without dependence on undocumented contractor knowledge.
Quantify benefits from approved analytics, automation, and AI initiatives, including hours eliminated, cycle-time reduction, rework avoided, data-quality improvement, cost savings or avoidance, and mission-risk reduction.
Provide the Program Manager and division leads with accurate status, risks, decisions, and upcoming milestones for program reporting and senior-leader review.