Job Summary

We are seeking an AI & Advanced Analytics Program Manager to lead a portfolio of fast-moving continuous-improvement initiatives, proof-of-concept projects, and emerging business requests.
The role will support a dedicated AI continuous-improvement team that operates similarly to an AI lab or AI factory. Unlike a traditional product team focused on one long-term strategic solution, this team will manage a changing pipeline of smaller projects, rapid proofs of concept, urgent executive requests, quick wins, and initiatives that may either scale or conclude after validation.
The Program Manager will be the primary business-facing contact for this portfolio and will coordinate business stakeholders, technical teams, contractors, and delivery partners to move initiatives from intake through execution, demonstration, assessment, and closure or scale-up.

Key Responsibilities

Demand intake and business engagement
•    Serve as the first point of contact for business stakeholders submitting AI, advanced analytics, and continuous-improvement opportunities.
•    Engage business teams to understand the problem, expected outcome, urgency, constraints, and success criteria.
•    Structure incoming requests into clearly defined projects or proof-of-concept initiatives.
•    Facilitate requirements-gathering and project-intake discussions.
•    Clarify project scope, stakeholders, dependencies, deliverables, and decision points.
•    Help distinguish quick-win opportunities from initiatives requiring broader strategic investment.
Program and project management
•    Manage a portfolio of concurrent AI, analytics, and continuous-improvement projects.
•    Develop project plans, milestones, delivery schedules, action trackers, governance routines, and status reports.
•    Coordinate rapid proof-of-concept delivery while maintaining visibility into scope, risk, quality, and stakeholder expectations.
•    Track project progress, dependencies, blockers, decisions, risks, and outcomes.
•    Ensure urgent initiatives move forward without disrupting the commitments of established product teams.
•    Coordinate projects that may scale after successful validation as well as projects that conclude after the proof-of-concept stage.
•    Maintain an organized view of portfolio demand, active work, resource needs, and upcoming priorities.
Team and contractor coordination
•    Coordinate internal teams, delivery partners, and contractors assigned to continuous-improvement initiatives.
•    Support the rapid mobilization and demobilization of project teams based on changing demand.
•    Align the appropriate technical capabilities to each business request.
•    Coordinate work across AI architects, data scientists, analytics specialists, developers, product managers, and business subject-matter experts.
•    Establish clear responsibilities, deliverables, interfaces, and escalation paths.
•    Help the dedicated continuous-improvement team operate independently while remaining aligned with the wider digital-thread portfolio.
Stakeholder communication and governance
•    Prepare and present project and portfolio status updates to leadership.
•    Communicate progress, outcomes, risks, decisions, resource needs, and recommendations clearly.
•    Facilitate steering discussions, working sessions, demonstrations, and project reviews.
•    Translate technical progress into concise business updates.
•    Maintain effective relationships with US-based business stakeholders and globally distributed delivery teams.
•    Escalate issues requiring leadership intervention and drive timely decisions.
•    Ensure expectations remain aligned throughout discovery, proof of concept, delivery, and closure.
Delivery and outcome management
•    Coordinate the delivery of AI and analytics proofs of concept within short execution cycles.
•    Ensure that each initiative has clearly documented objectives and completion criteria.
•    Organize demonstrations, stakeholder reviews, and outcome assessments.
•    Capture results, lessons learned, reusable assets, and recommendations.
•    Support decisions on whether a successful proof of concept should be scaled, transferred to a product team, enhanced further, or closed.
•    Promote repeatable delivery practices without limiting the flexibility required for experimentation and rapid execution.

Skill Requirements

•    Demonstrated experience project-managing or program-managing advanced analytics, artificial intelligence, machine learning, or data-driven initiatives.
•    Strong understanding of the AI and analytics project lifecycle.
•    Ability to lead requirement intake, business engagement, project planning, delivery coordination, status reporting, and executive communication.
•    Experience managing multiple simultaneous initiatives with changing priorities.
•    Strong stakeholder-management skills, particularly with business and leadership teams.
•    Ability to coordinate multidisciplinary technical teams without needing to serve as the primary solution architect or developer.
•    Excellent presentation, facilitation, written communication, and executive-reporting skills.
•    Strong risk, issue, dependency, and action-management discipline.
•    Ability to operate effectively in a flexible, fast-paced, proof-of-concept-oriented environment.
•    Experience working with contractors, service providers, or globally distributed delivery teams.
Preferred Technical and Domain Knowledge
•    Practical understanding of AI, machine learning, advanced analytics, and data-product concepts.
•    Familiarity with proof-of-concept delivery and rapid experimentation.
•    Exposure to manufacturing, continuous improvement, quality, shop-floor operations, or industrial use cases.
•    Familiarity with computer vision, equipment analytics, work planning and scheduling, or operational analytics.
•    Understanding of how AI initiatives progress from idea and discovery to proof of concept, validation, and scaled deployment.
•    Exposure to enterprise AI or analytics platforms, including partner-based platforms such as Palantir, is beneficial but not stated as mandatory.
•    Experience coordinating AI architects, product managers, data scientists, engineering teams, and business stakeholders.

Other Requirements

Recommended Education and Experience
The meeting does not define mandatory thresholds. For candidate screening, the following can be treated as recommended criteria:
•    Bachelor’s degree in Engineering, Computer Science, Data Science, Information Systems, Business, Operations, or a related field.
•    Relevant project or program management experience, including experience leading AI or advanced analytics initiatives.
•    Project-management certification such as PMP, PRINCE2, Agile, or Scrum is beneficial but should not replace demonstrated delivery experience.
•    Experience working in a US business-facing role and coordinating global teams is preferred.
Role Boundaries
The role will own
•    Business-facing intake and stakeholder engagement
•    Project and portfolio planning
•    Requirements coordination
•    Status reporting and leadership communication
•    Contractor and delivery-team coordination
•    Risk, issue, dependency, and action management
•    Proof-of-concept governance
•    Demonstration and outcome reviews
•    Recommendations for closure, continuation, or scale-up
Technical and portfolio leadership will retain ownership of
•    Overall digital-thread strategy
•    Enterprise AI architecture
•    Technical standards and architecture decisions
•    Product strategy for established solution areas
•    Final portfolio priorities and investment decisions
•    Detailed model, platform, and software engineering
Success Measures
•    New opportunities are assessed and mobilized efficiently.
•    Business requirements and expected outcomes are clearly documented.
•    Proofs of concept are delivered with transparent status, scope, risks, and decisions.
•    Leadership receives clear and timely portfolio updates.
•    Contractors and technical resources are aligned effectively to changing demand.
•    Continuous-improvement initiatives do not distract established product teams from existing commitments.
•    Project outcomes and recommendations are documented consistently.
•    Successful initiatives have a clear path toward scale, transition, or ownership.
•    Initiatives that do not demonstrate sufficient value are closed in a controlled and transparent manner.