Forward Deployed - Data & AI Product Manager

ShyftLabs

About The Position

ShyftLabs is seeking a client-facing Data & AI Product Manager to support a Quality AI initiative for a leading enterprise client. The role works as an extension of the client’s Data & Analytics team, partnering with Quality business stakeholders, technology teams, and implementation vendors to deliver AI-powered solutions. It is a hands-on product management opportunity focused on bridging business needs and technical execution, translating complex requirements into product deliverables, and ensuring solutions deliver measurable business value. The Product Manager provides product oversight, evaluates and challenges vendor recommendations, coordinates implementation activities, and helps ensure AI-enabled workflows are reliable, user-friendly, and aligned with business objectives. The role requires stakeholder management and delivery coordination skills, familiarity with enterprise data and analytics and emerging AI capabilities, and the ability to engage senior stakeholders and drive accountability in a complex enterprise environment.

Requirements

4–8 years of experience in product management, data and analytics, business analysis, digital transformation, or a related field.
Demonstrated experience supporting enterprise data, analytics, AI, or digital products from requirements definition through implementation and launch.
Strong client-facing experience and ability to build trusted relationships across business stakeholders, technical teams, and external vendors.
Experience reviewing technical deliverables, challenging proposed solutions, identifying gaps, and aligning business expectations with technical implementation.
Strong communication, facilitation, presentation, and stakeholder management skills, including the ability to navigate competing priorities and constructively challenge demanding stakeholders.
Experience coordinating UAT, business validation, issue resolution, and implementation readiness.
Ability to define success metrics, evaluate product performance, and connect delivery outcomes to measurable business value.
Strong organizational skills and attention to detail, with the ability to manage multiple workstreams in a fast-paced enterprise environment.
Ability to work independently while maintaining alignment with client stakeholders and cross-functional delivery teams.

Nice To Haves

Familiarity with Generative AI, document intelligence, optical character recognition (OCR), AI agents, or human-in-the-loop workflows.

Responsibilities

Serve as a product management partner to the client’s Data & Analytics team, bridging Quality business stakeholders, IT, data teams, and external implementation vendors.
Facilitate stakeholder discussions and requirements-gathering sessions; define priorities and translate business requirements into actionable product deliverables.
Coordinate day-to-day work across business and technical teams, aligning scope, timelines, dependencies, deliverables, and implementation priorities.
Communicate product progress, risks, decisions, and recommendations to business stakeholders and leadership.
Challenge assumptions, proposed solutions, and vendor deliverables to ensure they meet business requirements and enterprise standards.
Review vendor deliverables, including solution architectures, AI workflows, user interfaces, data mappings, testing strategies, and implementation plans.
Assess AI-enabled solutions for business alignment, effective human oversight, and appropriate transparency into findings, exceptions, and recommendations.
Partner with business and technology teams to shape interfaces that support document review, investigation of findings, assessment of exceptions, evaluation of AI-generated recommendations, and release decisions.
Coordinate business validation, user acceptance testing, defect resolution, and launch-readiness activities.
Track implementation risks, dependencies, open issues, and decisions, and drive timely resolution.
Support the transition from implementation to operational use and prepare stakeholders to adopt and use the solution effectively.
Define, challenge, and refine KPIs to measure the effectiveness and business impact of the Quality AI initiative.
Partner with the Value team to establish measurement frameworks, validate assumptions, and track benefits against agreed business objectives.
Translate business objectives into measurable product outcomes, targeting improvements in efficiency, quality, consistency, and decision-making.
Collaborate with data and analytics teams to validate data requirements, mappings, reporting needs, and information quality for AI-enabled workflows.
Maintain documentation of product requirements, decisions, risks, dependencies, action items, and delivery status.