Product Manager - AI/ML Marketplace

About the Role : Our team delivers premium matchmaking through dedicated Relationship Managers (RMs). We're building AI-powered systems that supercharge RM workflows - helping them identify better matches faster, spot member dissatisfaction early, and create exceptional member experiences at scale. As a Product Manager, you will own the strategy, roadmap, and execution of AI-driven products that blend human judgment with technology. You'll turn complex RM and member needs into simple, scalable experiences that create real business impact. Key Responsibilities : - Own the product vision, roadmap, and prioritisation for AI-powered experiences across the VIP member journey. - Discover high-impact problems through RM, member, data, and operational insights. - Translate RM workflows and member pain points into clear product requirements, user stories, and scalable features. - Build tools that help RMs with: 1. Member insights and profile intelligence 2. Matchmaking signals and recommendations 3. Engagement and dissatisfaction-risk detection 4. Conversation summaries and actionable context 5. Next-best actions and personalised interventions - Partner closely with engineering, data science, analytics, design, and operations to take products from idea to launch. - Define success metrics, run experiments, analyse outcomes, and continuously improve product performance. - Drive adoption of AI tools within RM workflows through sharp onboarding, feedback loops, and thoughtful change management. - Communicate product direction, trade-offs, and impact clearly to stakeholders and senior leadership. What You Bring : - 3 - 5 years of Product Management experience, ideally in data-driven, AI/ML-enabled, consumer, marketplace, CX, CRM, or workflow products. - Strong product sense, structured problem-solving, and analytical thinking. - Experience owning product roadmaps, prioritisation, PRDs, launches, and post-launch iteration. - Familiarity with AI/ML concepts and LLM-based applications - including how to assess quality, usefulness, and limitations. - Confidence working cross-functionally with engineering, analytics/data science, design, and operations teams. - Ability to use both user feedback and data to make decisions and measure impact. - Strong communication and stakeholder-management skills.