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Hebbia Ai

Forward Deployed Product Manager

NYC; San FranciscoOnsite · United States
FullTime · Product
POSTED 29 OCTVERIFIED 1D AGO
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Role
11 days opentrue age
Posted 29 Oct
Pay
$164k–$228kno pay posted
Market p25–p75
$164k–$228k
New York · n=107 · 365d to 6 Nov
CompanyHebbia Ai →
23open roles
Opened · 30d
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Closed · 30d
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Market
4,015open · Product
Opened · 7d
+2526% of open
All product
8,163open
All tracked boards · live 10 Nov · n=4,015

About Hebbia

The AI platform for investors and bankers that generates alpha and drives upside.

Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for 40% of the world's largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally.

We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets.

Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership.

The Role

We are looking for a Forward-Deployed Product Manager to help financial institutions put AI to work in their own workflows. You will own customer engagements from identifying the right problem and agreeing on success through implementation, rollout, and measurable business impact. You will work directly with customers and engineers to build solutions using Hebbia's products and platform.

You will be the product lead for your engagements, working with users, business sponsors, and technical teams to make decisions and keep delivery moving. This means spending time onsite, testing workflows with real data, and addressing constraints around integrations, permissions, and adoption. You should enjoy being accountable for a working solution and helping successful approaches become reusable across customers.

Responsibilities

Lead customer discovery to understand the business problem, current workflow, available data, and implementation constraints; agree on scope and measurable success criteria

Work with sales and customer teams to assess new opportunities, shape a feasible first engagement, and identify where AI can deliver meaningful value

Translate customer needs into requirements, workflow designs, prototypes, and delivery plans that engineers can build against

Partner with engineers on integrations, data access, permissions, and AI behavior; make product decisions and resolve dependencies throughout implementation

Lead the day-to-day delivery relationship with customer stakeholders, communicating progress, surfacing risks early, and managing changes to scope and priorities

Validate solutions against representative customer tasks, define acceptance criteria, and resolve quality and reliability issues before rollout

Own rollout, user enablement, and adoption; measure whether the deployed workflow delivers the agreed business outcome and address gaps

Identify patterns across engagements and work with product and engineering teams to turn successful solutions into reusable capabilities and deployment practices

Work onsite with customers and travel regularly for discovery, implementation, and rollout

Who You Are

Experience shipping software with engineering teams and independently leading customer implementations from discovery and scoping through launch and adoption

Experience delivering for customers under pressure, navigating conflicting stakeholder priorities, and constructively challenging requests to protect quality and outcomes

Technical fluency with APIs, integrations, and data systems, with the ability to investigate problems and make practical tradeoffs with engineers

Strong product and commercial judgment: able to define a valuable first release, assess delivery effort, and make clear recommendations with incomplete information

Clear communication with executives, end users, and engineers; able to explain technical constraints and build agreement on a practical plan

Hands-on use of AI tools to build prototypes, analyze data, and investigate workflow problems

Willingness to own the details of delivery, test the solution yourself, troubleshoot issues, and follow through until customers are using it successfully

Source · Hebbia AiFirst seen 4 Nov1 editsOriginal ↗