Product Manager Advisor - Data & AI Execution & Enablement

Federal Express Corporation; Plano (TX); Hybrid; USD 130,000 - 170,000; Full time

Job summary

Federal Express Corporation in Plano, TX, is seeking a Product Manager Advisor to lead the Data & AI Execution & Enablement program. You will manage end-to-end delivery of complex AI use cases, align business and IT stakeholders, drive governance, and enable scalable, reusable AI patterns across the enterprise.

You will own sprint cadences, weekly syncs, VoC programs, UAT governance, and executive storytelling.

Qualifications

• Dual hands-on delivery and portfolio governance experience.

Responsibilities

• Lead end-to-end AI use case delivery with cross-functional teams.

Skills

Project management

Stakeholder management

AI/ML lifecycle

Agile/Scrum

Executive storytelling

VoC management

UAT governance

Cross-functional leadership

Data delivery governance

Education

Master's degree in Computer Science, Data Science, Operations Research, Business Analytics, or MBA

Tools

Jira

Azure DevOps

Planview Agility

Job description

Product Manager Advisor - Data & AI Execution & Enablement

Domicile Information: This is a hybrid position located in Plano TX (first preference), Memphis, TN or Pittsburgh PA. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week.

Summary

Under limited supervision, this individual is responsible for:Driving both the overarching program governance for our AI delivery pipeline and the direct, hands-on project execution of our most complex, end-to-end Data and AI use cases from initial intake through production handoff.Independently partnering with commercial and enterprise business units to manage demand intake, ensuring business owners understand the delivery process, current status, and next steps while directly managing complex cross-functional builds.Leading communication management across all active use cases-establishing and running weekly operational syncs to proactively unblock engineering pods, capture delivery learnings, and systematically synthesize weekly achievements and roadblocks for upward reporting.Serving as the connective tissue across projects to identify technical redundancies, isolate high-value reusable components (such as prompt architectures, connectors, or models), and collaborate with technical leads to publish these standardized patterns for enterprise-wide use.Leading comprehensive upward executive reporting across all program lenses-aggregating and communicating training progress, holistic value delivery, Voice of the Customer (VoC) survey results, and execution cycle times. Managing stakeholder feedback loops by surveying program participants, actively improving survey response rates, analyzing VoC insights to refine delivery processes, and crafting compelling storylines and demo videos to showcase delivered AI capabilities to senior and enterprise leaders. Identifying and prioritizing process improvements, including Business Requirement Document (BRD) standardization and enterprise agility tool governance. Leading and executing complex AI development initiatives across large, cross-functional work teams, and actively coaching others in less senior positions.

About the Role - The Product Manager Advisor - Data & AI Execution & Enablement operates as a dual-hatted leader within our Data Science and AI department: you are both thearchitect of our overall AI delivery programand thedirect, hands-on project delivery lead for our most complex AI use cases. While business units and their technical counterparts retain long-term product ownership, you are responsible for managing the execution, deployment, and executive visibility of Machine Learning and Generative AI Minimum Viable Products (MVPs) built by our specialized teams of Data Scientists and AI Engineers.

Our department operates a flexible,three-tier service modelto deliver AI solutions for commercial and enterprise stakeholders:

• Advisory & Coaching:Guiding and upskilling business teams as they build their own analytical and AI solutions.
• Collaborative Co-Build:Partnering hand-in-hand with business and IT teams to develop solutions together.
• End-to-End Execution:Taking full ownership of complex technical builds from initial hypothesis to MVP handoff.

What You Will Do:

• Direct Project Execution (Hands-On Delivery):When high-complexity, end-to-end AI use cases enter our pipeline without dedicated project management coverage, you step in directly to lead them-managing sprint cadences, unblocking Data Scientists and AI Engineers, aligning stakeholders, and driving the build from kickoff to UAT sign-off.
• Lead Communication Management:Act as the operational drumbeat for the department. You will facilitate the weekly alignment meetings that bring our technical leads together, helping them share learning points, identify cross-project dependencies, clear roadblocks, and surface weekly achievements to leadership.
• Connective Tissue & Technical Reuse:Bridge the gaps between disparate development pods to eliminate duplicate engineering work. By keeping an active pulse on all active builds, you will identify high-quality, reusable assets and work with platform leads to publish them as standardized patterns for the entire organization to leverage.
• Program Governance, VoC & Scale:Across the broader portfolio, you bring predictability and structure to our multi-wave delivery cycle. You will own the master delivery tracker, standardize execution playbooks, and manage our Voice of the Customer (VoC) program-surveying participants, boosting response rates, and turning feedback and retrospectives into immediate process improvements.
• 360-Degree Executive Reporting & Storytelling:Synthesize and report upward to senior executives across every dimension of our program:training and self-service progression, realized business value and ROI, VoC sentiment, and execution cycle times. Additionally, you will help craft compelling narratives anddemo videosto showcase our delivered AI use cases across the enterprise.

Core Responsibilities

• Hands-On Management of Complex AI Use Cases:Step in as the dedicated, day-to-day project delivery lead for complex, high-priority Data Science and Generative AI use cases (especially End-to-End builds). Manage timelines, dependencies, technical risks, and daily execution across Data Scientists, AI Engineers, and business partners to ensure on-time MVP delivery.
• Active Communication & Alignment Management:Design, lead, and optimize the department's weekly synchronization cadences (including weekly use case lead syncs and team-wide alignment meetings). Proactively identify cross-project blockers, extract immediate lessons learned, and systematically package weekly achievements and critical hurdles for executive review.
• Technical Asset Reuse & Redundancy Reduction:Act as the connective tissue across all development pods. Review active codebases and delivery architectures to identify redundant development efforts, isolate the highest-performing engineering patterns, and coordinate with platform leads to publish these reusable components (such as specific models, prompt-engineering pipelines, or custom API connectors) for broad platform adoption.
• Program-Wide Delivery Governance & VoC Management:Serve as the operational backbone for the department's overall multi-wave AI pipeline. Establish predictable delivery cadences, track adoption across our three service models (Coach, Co-Build, End-to-End), and guide partner teams as they mature toward self-service capabilities.Survey program participants, drive initiatives to increase survey response rates, report out Voice of the Customer (VoC) and NPS results, and translate stakeholder feedback into measurable process improvements.
• 360-Degree Upward Executive Reporting:Deliver comprehensive, multi-dimensional upward reporting to senior and executive leadership. Synthesize and present program health through all critical lenses-includingpartner training and enablement progress, holistic value delivery, VoC sentiment, and end-to-end execution cycle times.
• Use Case Storytelling & Demo Production:Partner with technical pods and business stakeholders to craft high-impactstorylines and demo videosaround completed AI use cases, bringing our technical capabilities to life for enterprise stakeholders and senior executives.
• Portfolio Value, ROI & FinOps Aggregation:Collaborate with the technical execution pods-who estimate and advise on use-case-level cloud costs and FinOps metrics-to roll up, aggregate, and report holistic program value. Quantify how the overall portfolio is impacting revenue growth, operational efficiency, and customer experience.
• Demand Intake & Stakeholder Alignment:Operate a transparent, structured demand intake process with commercial and enterprise business teams. Ensure use case owners clearly understand where their requests stand, what prerequisites are required, and the next steps to move from intake to active development.
• Playbook & Artifact Standardization:Own and continuously refine the department's central execution playbooks and templates. Collaborate with technical leads to translate real-world project learnings into step-by-step execution guides, Business Requirement Document (BRD) templates, technical specification guidelines, and cloud onboarding documentation.
• Formalized UAT & Sign-Off Governance:Standardize and oversee User Acceptance Testing (UAT) across AI deliverables. Maintain centralized UAT test planning frameworks, sample test case documents, defect logs, and formal stakeholder sign-off procedures.

Knowledge, Skills, and Abilities

• Dual-Level Execution (Project & Program):Proven ability to zoom in and manage the granular, day-to-day technical execution of a complex AI build, while simultaneously zooming out to govern portfolio-wide processes, playbooks, and KPIs.
• Operational Communication Management:Demonstrated excellence in structuring and facilitating technical coordination meetings that maintain alignment across multiple engineering pods, uncover delivery risks early, and generate clear executive briefings.
• Portfolio-Level Connective Tissue:Outstanding technical pattern-matching skills with the ability to identify redundant workflows across separate project teams and advocate for standardizing and publishing reusable components.
• Executive Reporting & Storytelling:Exceptional communication and presentation skills with the ability to synthesize multi-dimensional metrics (training, value delivery, FinOps, VoC, and cycle times) into crisp executive readouts, as well as the creative knack to scriptcompelling use case narratives and demo videos.
• VoC & Process Engineering:Experience designing and managing stakeholder feedback loops (surveys, NPS/VoC analysis), driving high participation rates, and turning qualitative/quantitative feedback into repeatable playbooks, BRD templates, and UAT frameworks.
• Matrixed Stakeholder Management:Strong influencing skills to align data scientists, AI engineers, enterprise platform architects, and non-technical commercial leaders without relying on direct authority.
• AI & Machine Learning Lifecycle:Solid conceptual understanding of Generative AI architectures (LLMs, RAG, agentic workflows), traditional machine learning, and Operations Research-specifically understanding what it takes to move an AI use case from requirements and cloud onboarding through model development, UAT, and production handoff.
• Agile & Portfolio Tooling:Mastery of Agile/Scrum ceremonies tailored for iterative data science teams, along with hands-on proficiency in enterprise tracking tools such asPlanview Agility, Jira, or Azure DevOps (ADO).
• Financial & Value Literacy:Working knowledge of cloud FinOps concepts and business value frameworks, enabling effective collaboration with engineering leads to aggregate and report portfolio-wide cost-to-value metrics.
• Professional Certifications (Highly Preferred):PMP(Project Management Professional),Agile/Scrum certifications(CSM, PMI-ACP, SAFe), orEnterprise Agile Portfolio Managementcredentials.

Preferred Qualifications

• Education:Master's degreein Computer Science, Data Science, Operations Research, Business Analytics, or anMBA with a technical/analytical focus.
• Redund