AI Product Manager, YipitData

• 4-6+ years of product management or similar experience
• Experience building AI copilots, agents, or internal AI tools
• Familiarity with how AI/ML models and AI agents are built and maintained
• Strong product instincts with a track record of building 0→1 or ambiguous products
• Strong user research and customer discovery skills with deep curiosity about AI systems (LLMs, agents, retrieval, evals)
• Generalist knowledge of both engineering and design
• Able to design and evaluate prompts, contexts, and model behaviors
• Analytical thinker, comfortable with data and experimentation
• Great communicator and collaborator
• High ownership mindset with a bias toward action and learning

Desirable

• We’re hiring an AI Product Manager to build the next generation of investor-facing products powered by AI
• You’ll sit on the Public Investor team, owning products that help institutional investors turn complex data into faster, higher-conviction decisions. This includes AI-powered workflows across:
• MCPs (Model Context Platforms)
• AI agents for research and analysis
• Investor-facing portal experiences
• Data feeds and programmatic access
• This is not a traditional PM role. You’ll operate at the intersection of AI capabilities, user workflows, and proprietary data, defining how cutting-edge models translate into real investor value
• You’ll work directly with investors, researchers, and engineers to design systems that don’t just surface insights, but actively assist, automate, and augment decision-making. We have unmatched data. What we need is world-class user experience to unlock it—so customers can turn our insights into value at scale. That’s where you come in
• Own AI product strategy for investor workflows: Define how AI transforms the way public market investors consume, analyze, and act on data
• Build AI-native products, not just features: Design end-to-end experiences across agents, copilots, and structured outputs, not just UI layers on top of models
• Translate frontier AI into real-world utility: Evaluate and integrate LLMs, retrieval systems, and agent architectures into production-grade products
• Work directly with customers: Partner with hedge funds and asset managers to deeply understand workflows, pain points, and trust requirements
• Drive rapid experimentation and iteration: Run tight feedback loops across prompts, evals, and product UX to continuously improve performance and usefulness
• Collaborate across disciplines: Work closely with engineering, data, research, and design to ship high-quality, high-impact products, while communicating clearly with both technical and non-technical partners
• Define quality, reliability, and trust: Establish evaluation frameworks for AI outputs, ensuring accuracy, consistency, and investor-grade reliability
• Shape the future of the platform: Contribute to how MCPs, agents, and APIs evolve into a unified, reliable, and safe AI product ecosystem