Senior Product Manager, Applied AI — Financial Markets

London Stock Exchange Group•New York City, NY

11h•Remote

About The Position

We're hiring someone to lead applied AI products for our markets business — and we're looking for a specific combination that's rare by design. We believe the next generation of products in this space will be built by people who understand intelligence itself — how humans and machines represent problems, form judgments under uncertainty, and decide — and who have proven they can turn that understanding into products that ship, scale, and sell. If you've made the journey from studying how minds or models work to building things people rely on every day, this role was written for you.

Requirements

4–8 years building and shipping products, with 2+ years on AI/LLM-powered products in production.
Owned outcomes in a commercial setting, not only studied the problem.
Genuine depth in how intelligent systems — human or artificial — represent, learn, and decide.
Current engagement with the field: you read the papers, experiment with new models and tools as they emerge, and hold informed opinions about where this is going.
Fluency in modern AI building blocks (LLMs, RAG, agents, evals), with the hands-on instinct to prototype your own ideas.
Evidence of unconventional thinking that worked — a result, product, or prototype where you saw something others missed.

Nice To Haves

An advanced degree or publications in ML, neuroscience, cognitive science, or a related field.
Experience with financial data, analytics, or other regulated/high-stakes domains.
A portfolio of side projects or prototypes.

Responsibilities

Shape and drive strategy, roadmap, and delivery for your AI product area — from user discovery through production launch and commercial outcomes.
Develop a rigorous understanding of how our users reason and decide, and translate it into AI products that measurably improve their outcomes.
Identify where modern AI genuinely beats conventional approaches across markets workflows — and where it doesn't. We expect evidence, not enthusiasm.
Form your own product hypotheses, prototype them quickly, and back the ones the data supports.
Ship responsibly in a regulated environment: define evaluation standards, guardrails, and auditability appropriate to financial markets.
Stay close to the research frontier — in AI and in the science of cognition and behavior — and bring what's coming into our thinking early.