AI Product Manager

YipitData is the leading market research and analytics firm for the disruptive economy, and they are seeking an AI Product Manager to build the next generation of investor-facing products powered by AI. The role involves owning AI product strategy, designing AI-native products, and collaborating with various teams to enhance user experience and product reliability.

Responsibilities:

• 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

Requirements:

• 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
• Familiarity with financial markets or investor workflows
• Experience with data platforms, APIs, or developer-facing products
• Background in analytics, research, or quantitative environments