Job Specifications
Key Responsibilities
Product Strategy & Discovery
Define product vision, value proposition, and success metrics for AI use cases (e.g., copilots, recommendations, document intelligence, forecasting, decision support).
Conduct discovery to validate problems and opportunities: user research, data readiness, feasibility spikes, and hypothesis testing.
Build business cases and prioritised roadmaps aligned to portfolio goals and budget constraints.
Delivery & Execution
Translate strategy into clear, outcome‑oriented roadmaps, slicing value into increments (MVP → V1 → scale).
Own and refine the product backlog: epics, user stories, acceptance criteria; manage scope, dependencies, and risks.
Partner with Data/ML teams to align model goals (precision/recall, latency, drift tolerance) with user and business outcomes; plan model release/rollback strategies.
Ensure ML Ops practices are in place (observability, monitoring, retraining, A/B testing, human‑in‑the‑loop where required).
Drive high‑quality experimentation: define guardrails, run controlled trials, and make evidence‑based go/no‑go decisions.
Stakeholder & Change Management
Communicate progress, risks, and decisions to senior stakeholders; manage trade‑offs across compliance, security, cost, and experience.
Coordinate change enablement (training, comms, process updates) to ensure adoption and benefits realisation.
Responsible & Compliant AI
Embed Responsible AI principles: fairness, transparency, privacy, safety, and accountability.
Work with Legal, Risk, and Security to meet data protection and sector‑specific compliance; maintain model and data governance artefacts (data lineage, evaluation reports, decision logs).
Required Experience & Skills
Product Leadership
5+ years in product management (or adjacent roles: product owner, head of product, venture builder) delivering data‑ or AI‑enabled products at scale.
Proven track record of shipping outcomes (not just features) with quantifiable KPIs (revenue uplift, cost avoidance, cycle‑time reduction, NPS/CSAT, adoption).
AI / Data Literacy
Practical experience taking at least one AI/ML product from discovery to live usage (classification, NLP, recommendation, forecasting, agentic workflows, or GenAI).
Ability to frame model objectives (e.g., F1 vs. accuracy trade‑offs), evaluate experiment results, and translate them for business stakeholders.
Familiarity with data pipelines, data quality, model monitoring, and drift remediation; understands the implications of PII/PHI and privacy‑by‑design.
Execution & Ways of Working
Strong command of agile/product delivery (Scrum/Kanban), backlog management, and cross‑team dependency handling.
Skilled in hypothesis‑driven development, A/B testing, and product analytics (e.g., Amplitude, GA4, Mixpanel, custom telemetry).
Stakeholder Influence
Excellent communication, storytelling, and negotiation skills; comfortable engaging executives, SMEs, and technical teams.
Experience in regulated or complex environments (e.g., government, FS, healthcare) and navigating governance forums.
Tooling
Hands‑on with Jira/ADO, Confluence, Miro/FigJam; comfortable reviewing dashboards in Power BI/Tableau; able to interpret notebooks/outputs from DS teams.
Desirable
Experience with Generative AI products (RAG, prompt engineering, evaluation frameworks, safety filters, content governance).
Exposure to Azure/AWS/GCP AI stacks (e.g., Azure OpenAI, Bedrock, Vertex AI), vector databases, feature stores, and ML Ops platforms.
Understanding of design systems and service design; experience with human‑in‑the‑loop workflows.
Domain experience in public sector, financial services, energy, or retail.
Certifications: Pragmatic, AIPMM, SAFe POPM, or cloud data/AI certifications.