- 5+ years of product management experience, with at least 2 years on a B2B SaaS or internal tooling product used by knowledge workers
- Demonstrated ability to own a roadmap end-to-end: discovery, prioritization, spec writing, and post-launch iteration
- Strong written communication - specs, PRDs, and async updates that are clear enough that engineering rarely needs to ask follow-up questions
- Comfort working in a technical environment: understanding API integrations, async job pipelines, and AI-powered features well enough to make scoping decisions without daily engineering hand-holding
- Experience using product analytics tools (PostHog, Mixpanel, Amplitude, or equivalent) to measure impact
- Organized and process-driven - able to manage a backlog, run sprint ceremonies, and keep multiple streams of work on track simultaneously
Nice-to-Have
- Background in or exposure to financial services - investment banking, M&A advisory, Venture Capital, Private Equity, or asset management
- Familiarity with deal workflows: pipeline management, deal stages, company screening, CIM review, or due diligence processes
- Experience expanding a product into a new vertical or user segment
- Hands-on experience with document-heavy or AI-assisted workflows
- Comfort reading a data model or reviewing a SQL schema - enough to gut-check feasibility of a feature without a full engineering deep-dive
We are building a deal intelligence platform used by M&A advisors and alternative asset managers - including Venture Capital and Private Equity firms - to manage pipelines, process deal documents, and surface AI-driven company insights. We're looking for a Senior Product Manager who can own the product end-to-end: shaping roadmap strategy with stakeholders, writing tight specs a small engineering team can ship against, and driving the platform's expansion across both M&A advisory and Funds workflows.
Key Responsibilities
- Own the roadmap - maintain a prioritized backlog, make trade-off calls, and communicate direction clearly to engineering, design, and business stakeholders
- Drive discovery - conduct user interviews with bankers and analysts, synthesize feedback into actionable problems, and validate solutions before committing engineering time
- Write precise specs - produce feature briefs and acceptance criteria that leave no ambiguity; translate workflow nuances (pipeline stages, deal classification, document review flows) into requirements engineering can build against
- Create HTML Mockups - use Claude or other LLMs to generate HTML mockups to verify with the customer and handover to engineers
- Ship iteratively - define and manage feature flags for gradual rollouts; track adoption metrics via PostHog and use data to drive iteration
- Steer AI features - work closely with engineering to shape how AI agents process documents, enrich company data, and surface deal recommendations; understand enough about the underlying models to set realistic expectations and spot failure modes
- Expansion to Funds - lead discovery and scoping for the new Funds vertical (VC, PE, family offices); identify where the existing M&A platform transfers and where new workflows, data models, and integrations are needed
- Manage stakeholder expectations - communicate progress, surface blockers early, and keep the deal team aligned with the product direction
