Product Manager

Edisyl•Boston, MA

1d•Remote

About The Position

edisyl builds AI solutions that turn messy institutional data into decisions, workflows, and outcomes. We came out of blockchain data infrastructure — 8 years, 20+ chains, 700M+ resolved wallets — and now deploy that capability to enterprises navigating the same challenge: how to make their data work for them at scale, without armies of analysts. We have active deployments with a financial institution and Interlochen, a proven architecture, and inbound from firms that need what we've built. The technology works. What we're building now is the enterprise motion around it. This role is for someone who has owned a product line end-to-end, not just the process around one. You know the difference between running discovery calls and actually changing your plan because of what you heard on them. You've shipped something where the whole fight was making it feel effortless, not making it feature-complete. You're not looking for a defined roadmap to execute. You're looking for the open questions — provenance, version propagation, what belongs public versus private — and the room to drive them from fuzzy to decided yourself. You live in AI tools day to day — comfortable building in Claude Code or similar — and you track the rough edges of what's possible right now. Not so you can build the flashiest thing, but because the actual job is taking what's dense and technical underneath and making it disappear for someone who isn't AI-native themselves. If a customer's daily tool of choice is a basic chat window, effortless is the whole bar.

Requirements

~7+ years in product, with senior end-to-end ownership of a product line or surface
Owned a roadmap and been accountable for outcomes (adoption, retention, growth), not just shipping
Shipped a product where simplicity or ease-of-adoption was the core challenge
Has driven real AI/product adoption inside large organizations, not just shipped a pilot
Worked closely with engineering and data science on a technical product, without needing the infrastructure translated
Roadmap and sequencing judgment. You can turn a vision and a pile of market signals into a plan where each build sets up the next. Without this, a team ships well but doesn't get anywhere.
Product taste. Our whole bet is consumer-simple feel over hard infrastructure underneath. You relentlessly cut toward effortless rather than defaulting to feature-completeness — this is the rarest, least teachable thing we're hiring for.
Real discovery instincts. Not running calls for their own sake, but hearing something you didn't already believe and changing course because of it.
Backbone, delivered as consensus, not decree. You push back on vision — and on engineering's default architecture — when the market or the product disagrees with it. You get there by asking the right questions and bringing people along, not by declaring "do it this way."
Product thinking over process. You're organized because it drives value to customers fast, not because a granular plan feels like progress. You'd rather write a direction doc that lets the team generate its own tickets than write every ticket yourself.

Nice To Haves

Consumer or prosumer product experience where ease-of-adoption was the whole game
Product shipped to large-enterprise or Fortune 500 buyers
Founder or early-stage operator experience
A 0-to-1 product built, not only scaled

Responsibilities

Own the long-term product roadmap, translating vision into a sequenced plan where each build deliberately compounds the next, and driving alignment on it across leadership and the team
Run continuous customer and market discovery, maintaining ongoing contact with users and the market, synthesizing what comes back, and feeding it into roadmap decisions as a standing input, not a one-off
Drive open product-architecture decisions to resolution — provenance, the public/private seam, version propagation across packs, consumer-facing vocabulary — from fuzzy to decided
Think through the in-the-weeds product architecture — the specific edge cases, like how access or permissions behave under a given condition — before engineering picks it up, so the details are already decided rather than discovered mid-build
Define and prioritize what gets built: write the specs, set sequencing, and make the prioritization calls that keep a self-managing team pointed at the compounding plan
Partner closely with engineering and data science on knowledge-base quality, and with sales and customer success on what's closing, where deals stall, and what drives adoption.