Agile Product Manager

Role Overview

We are looking for an experienced Agile Product Manager with over a decade in product management, specializing in AI projects. This role involves owning the outcome of AI engagements by translating business objectives into prioritized and actionable backlogs, defining success for inherently uncertain AI systems, and ensuring successful adoption after deployment.

Key Responsibilities

• Maintain and prioritize the product backlog focused on AI initiatives, organizing epics by value, feasibility, and associated risks.
• Transform business goals into tailored AI use cases with precise success criteria and acceptance benchmarks.
• Conduct discovery sessions with clients and users to distinguish genuine issues from AI-driven solutions.
• Collaborate with AI Assurance and clients to define acceptable performance standards for non-deterministic systems.
• Manage release planning across proof of concept, pilot, and scaling phases, including human-in-the-loop and fallback mechanisms.
• Prioritize data preparation and integration tasks concurrently with model development efforts.
• Lead sprint ceremonies, ensure smooth progress by shielding the delivery team from impediments, and uphold a unified priority set.
• Oversee project scope, timelines, and client expectations amid uncertain model performance.
• Drive adoption and change management by organizing training, communications, and tracking actual usage post-launch.
• Monitor and report value realization following launch to influence ongoing product roadmaps.
• Ensure responsible AI practices and governance compliance are integrated into the delivery workflow in collaboration with governance and security teams.
• Communicate progress, risks, and achievements clearly to both clients and senior leadership within the firm.

Candidate Requirements

• At least 10 years in product management or ownership roles, including a minimum of 3 years focusing on AI, machine learning, or data-centric products.
• Proficient in agile methodologies such as Scrum, Kanban, or SAFe, with the ability to manage backlogs under tight deadlines.
• In-depth understanding of the AI delivery lifecycle, including data readiness, model evaluation, deployment, monitoring, and retraining.
• Technically savvy enough to critically assess feasibility, cost, and latency claims without hands-on design responsibilities.
• Capable of drafting acceptance criteria tailored for probabilistic AI outputs, defining thresholds, safety measures, and escalation procedures.
• Knowledgeable about AI evaluation and observability methods sufficient to interpret and act upon findings.
• Familiar with responsible AI practices, privacy considerations, and governance protocols in regulated industries.
• Strong facilitation and discovery abilities, including conducting workshops, stakeholder interviews, and prioritization with senior client representatives.
• Data-aware regarding outcome measurement through instrumentation, adoption analytics, and benefit realization tracking.
• Effective communicator able to maintain client focus and confidently decline scope expansions unlikely to deliver value.
• Experience managing change management and adoption processes for solutions that transform user workflows.

Success Metrics

• Achieved business value relative to the original business case.
• Levels of adoption and active usage following product launch.
• Accuracy and predictability of releases from proof of concept through to scaling.
• Client feedback reflecting clear understanding of scope and priority settings.