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.