Our client, an AI fintech startup, is building a small, elite team of Applied AI Analysts: seasoned finance practitioners from fundamental research, data science, and portfolio and risk management. These analysts will work directly with the company's clients to understand their workflows and fundamentally transform them using the company's AI platform. This is a finance role at the frontier of AI adoption.

When a client engages the company, they get an AI platform deployed in their own cloud, with their data, their environment, and their governance. You'll sit alongside their investment professionals, learn how they work, and build AI workflows within the platform that transform how they operate.

You'll also be one of the company's sharpest feedback signals. You'll surface what's working, what's missing, and what should be built next, and partner with product and engineering to shape the platform.

Responsibilities:

Embed with clients and drive AI-led workflow transformation. Work on-site with PMs and research analysts to understand their current workflows, find high-leverage opportunities for automation and augmentation, and build production-grade AI workflows that transform their processes.

Translate client workflows into skills, agents, and connectors. Partner with engineering to turn what you learn on the ground into new data connectors, skills, and agents. You're the bridge between how investment teams think and how the platform is built. Your domain fluency is what makes that translation precise.

Contribute to the product feedback loop. Use the platform daily and bring structured, actionable feedback to the product team on new features.

Build the platform's knowledge base for your domain. Contribute to a library of skills and agents covering best practices in research, risk, operations, data science, and technology. These skills work together as a single knowledge base that lets AI connect technology, data, research, risk, and trading in one platform. You'll own the playbooks the company deploys and continuously improves across every client relationship.

Must-Have:

A proven track record in a core equity research function (fundamental research, quantitative research, or data science) at a top-tier financial institution

Avid, everyday use of AI tools, built into your workflow rather than just experimented with, with strong views on what works, what breaks, and where the leverage is

Qualifications:

3 to 7 years of experience in a core equity research function at a top-tier hedge fund, asset manager, sell-side research firm, RMS vendor platform, or institutional investment fund

Multiple hands-on projects building with AI (for example, a GitHub repository of research skills, side projects, or other practical AI work)

Hands-on experience with financial data vendors such as Bloomberg, Visible Alpha, FactSet, PitchBook, or CapIQ

Ability to build trust quickly, ask the right questions, and handle unstructured conversations with senior investors and C-suite executives at top hedge funds

Nice-to-Have:

Experience across more than one investment discipline (for example, research and risk, or research and quantitative methods), or client-facing / forward-deployed experience

Technical skills with data APIs or MCP connectors, SQL/Python for data processing, core libraries, and/or agentic AI frameworks