Reflexivity builds an AI-native investment analysis platform for institutional investors, combining trusted financial data, knowledge graphs, document intelligence, and explainable AI to surface actionable insights instead of noise. Alfred, our financial reasoning engine, helps investment teams move from question to evidence-backed analysis faster - across research, screening, portfolio insights, scenario analysis, and partner integrations.
Why this role existsOur data squad sits at the center of some of the company's most important relationships with major partners. Reflexivity consumes partner data across pricing, M&A, corporate events, fundamentals, ownership, news, and text documents - and also packages Reflexivity capabilities back into partner products, improving their surfaces with the intelligence we have built.
The PM who built this motion is leaving for business school. We are looking for a sharp, technically fluent product owner to take it over, raise the bar, and keep the system scaling.
What you'll ownYou will lead the data squad - four engineers, two Python and two Golang - and act as the day-to-day product owner for the data and product flows between Reflexivity and major partners.
The work splits roughly two ways, and today it leans outbound:
- Outbound, the majority of the role today: Take capabilities built inside Reflexivity and ship them into partner products. You will work closely with partner product and engineering teams to decide what to integrate, map their constraints to ours, and get production-grade functionality live inside someone else's environment.
- Inbound: Keep refining how Reflexivity ingests, models, and uses partner data on our own platform. You will own data-model mapping, business logic, and the QA bar. Near-term examples include ingesting MCP servers, moving select feeds from APIs to FTPs, sharpening entity resolution and coverage universes, and continuing to find efficiencies in high-volume data workflows.
- Run a working session with a partner engineering team to align on schema mapping for a new dataset
- Write a crisp spec for engineers on a corporate-actions edge case
- QA last week's release against ground truth and decide what ships versus what holds
- Partner with GTM on how to explain a coverage universe to clients
- Use AI tooling such as Cursor, Claude, or Windsurf to prototype business logic before handing it to engineering
- Make a judgment call on whether to push back on a partner ask or absorb it into the roadmap
- 3-5 years as a PM, TPM, or technical/data role with PM-shaped responsibilities. We do not need senior; we need sharp.
- Genuine technical fluency. You can read schemas, reason about APIs and data pipelines, talk to engineers as peers, and write specs that backend engineers can execute without multiple clarification rounds. You will not write production code.
- Comfort running external partnerships. You can lead a working session with another company's team and walk out with decisions, not vague action items. You can read the room when their internal constraints or politics are affecting the work.
- High tolerance for ambiguity. Financial data has a long tail of odd business rules and undocumented edge cases. You should enjoy chasing them down rather than waiting for someone else to define them.
- Daily user of AI assistants. You should already use Cursor, Claude, Windsurf, or similar tools to prototype logic, explore data, and codify business rules - not just to write emails. This is how the team works.
- Strong written communication. Specs, partner-facing docs, internal updates, release notes - the role is half writing.
- A QA mindset. You think about how systems break before they break, and you build the muscle to catch regressions early.
- Background in financial data - market data, fundamentals, corporate actions, ownership, news, research, or alternative data from providers such as Bloomberg, FactSet, S&P Global, Moody's, ICE, Nasdaq, Cboe, or similar
- Experience as a data or technical PM at an early-stage startup, where the role spans well beyond its formal description
- CS, math, finance, or quantitative degree - or a self-taught track record that proves the same thing
