Director of Product

Reporting to

Chief Product Officer (CPO)

OVERVIEW OF 73 STRINGS:

73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.

Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.

The Opportunity

Alternative asset management has yet to be fundamentally reshaped by AI and 73 Strings is positioned to drive that change.

The workflows our customers rely on - ingesting unstructured data from portfolio companies, deriving inputs for the valuation process, generating investor reports - are exactly the kind of complex, manually-intensive processes where agentic AI can deliver transformative improvements. While our complex deterministic workflows, robust fair value calculations, auditability and governance, and single version of truth provide both a deep moat and a springboard to deploy AI broadly to automate funds workflows.

This creates a significant opportunity to expand what 73 Strings does for its customers and a correspondingly large increase in the value we capture. We need to move decisively— building AI products that expand our platform's value, and ensuring we stay ahead of the disruption curve.

We need a builder-leader who has shipped production agentic systems and knows exactly what it takes to do it again — at scale, in a regulated industry, with zero tolerance for hallucination.

About the Role

You will manage some of the ley initiatives of AI product management at 73 Strings, reporting to the Chief Product Officer. You will help refine our AI product strategy, build and execute it with rest of the team, and be accountable for shipping products that materially expand what our platform can do for customers.

This role demands a rare combination: someone who can set the strategic vision and then open a code editor to prototype it the same afternoon. You will own the AI product roadmap and be the person on the team who most deeply understands the technical substrate — how agents fail, where latency hides, why a workflow that works in a notebook breaks in production. You will talk to customers in the morning and be tinkering with a new orchestration pattern or evaluation framework by the afternoon.

Customer engagement and research

• Develop deep, first-hand understanding of how alternative asset managers work — their workflows, pain points, and decision-making processes.

• Engage directly with customers and prospects to identify where agents and AI-driven capabilities can solve real problems and create measurable value.

• Translate qualitative customer insight and quantitative usage data into a clear, data-driven picture of what to build and why.

AI product strategy

• Define the AI product vision and roadmap: which products and features to build, in what sequence, and why.

• Couple deep understanding of customer needs with a working knowledge of what is technically feasible with current and near-future AI capabilities - particularly agentic architectures, LLMs, and structured data extraction.

• Make prioritisation decisions that balance short-term customer value against long-term platform differentiation.

Design, build, and ship

• Work with engineering, design, data, subject matter experts, and commercial teams to take AI products from concept through to launch.

• Own the end-to-end product development lifecycle: requirements, design, ultra-rapid prototyping, development, testing, launch, and iteration.

• Champion a data-driven approach to product development: define success metrics, run experiments, and iterate based on evidence.

• Establish best practices around AI governance, reliability, and safety i