Product Manager

A.      Product Strategy & Roadmap

·       Define and maintain the product vision and roadmap for selected digital mental health and AI-enabled initiatives

·       Translate strategic objectives into prioritised backlogs, epics and user stories with clear acceptance criteria.

·       Review and adjust roadmap based on data, user feedback, and system constraints.

B.      AI Product Design & Governance

·       Define use cases for AI-enabled features (e.g., co-pilot tools, triage support, recommendation engines) grounded in clear problem statements and measurable outcomes.

·       Work with data science and engineering teams to translate model capabilities and limitations into usable, safe product features.

·       Ensure human-in-the-loop safeguards, escalation logic, explainability elements, and appropriate UX disclosures are embedded into product design.

·       Define model evaluation metrics (e.g., precision/recall for risk alerts, response quality benchmarks, latency thresholds) and acceptance thresholds prior to deployment.

·       Oversee responsible AI governance, including bias risk assessment, data provenance considerations, auditability, and monitoring for model drift.

C.      Delivery & Cross-Functional Coordination

·       Lead sprint planning, backlog grooming and prioritisation in collaboration with engineering, data science, UX, and operations teams.

·       Define clear product requirements documents (PRDs), including data flows, edge cases, risk scenarios and non-functional requirements.

·       Ensure alignment between frontend, backend, analytics and ML components.

·       Coordinate UAT, stakeholder reviews, and release readiness sign-offs.

D.     Data, Metrics & Impact Tracking

·       Define north-star and operational KPIs (e.g., engagement, completion rates, workflow efficiency, response times, escalation accuracy).

·       Work with analytics teams to measure user behaviour and AI system performance.

·       Interpret data to guide iteration, feature sunsetting, or scaling decisions.

·       Ensure product decisions are evidence-informed and evaluation-ready.

E.      Documentation, Risk & Transition

·       Maintain comprehensive documentation of product requirements, workflows, system dependencies and governance controls.

·       Develop knowledge transfer artefacts to support long-term sustainability and ownership transitions.

·       Identify operational, clinical and reputational risks associated with AI-enabled features and implement mitigation plans.

·       Support audit, compliance, and security review processes.

F.       User Research & Validation

·       Conduct user research across help-seekers, counsellors, clinicians and administrators to validate needs and usability.

·       Design and oversee pilots and controlled rollouts of new AI-enabled capabilities.

·       Translate qualitative and quantitative findings into actionable product improvements.

·       Ensure continuous feedback loops are embedded into product lifecycle.

·       Coordinate with research partners to run evaluation studies of the deployed system.