Equity Research Analyst, AI Platform Equity Analysis

VERSANT is an independent, publicly traded company that brings together powerhouse brands such as CNBC, MS NOW (formerly MSNBC), USA Network, Oxygen, E!, SYFY, and Golf Channel along with dynamic digital and direct-to-consumer brands such as Fandango, Rotten Tomatoes, Golf Now, Golf Pass, and Sports Engine.

Stock Story, a newly acquired part of CNBC, is building the next generation of AI-powered equity research for individual investors. We continue operating like a startup: small team, high ownership, fast decisions, direct access to leadership, and a culture focused on building rather than managing.

At the same time, we have the backing of a large, well-capitalized public company with iconic brands, meaningful free cash flow, and a long-term commitment to investing in data, technology, and our team. That means the upside of a fast-paced builder environment without the fragility and resource constraints of a typical early-stage startup.

Our mission is to help millions of investors achieve better results in the markets. This is your chance to work at the intersection of investing, AI, and product, alongside other exceptional engineers and investors, and help define what modern equity research looks like.

We're looking for a highly driven and intellectually curious equity analyst to join our team to build a new generation of AI-powered equity research.

This role is designed for investors who still want to apply and sharpen their fundamental research judgment, but who are more excited by building scalable systems than by repeating the same research process company by company, quarter after quarter. It is especially well suited to people who want to future-proof their investing career by learning how great equity analysis gets translated into software, models, and AI-enabled products.

You will be instrumental to the team that builds and operates core components of our AI equity research engine: LLM workflows that convert unstructured information into structured signals, and factor models that turn those signals into consistent, scalable outputs for individual investors.

This role sits within a small and growing, high-caliber unit where team leaders boast Hedge Fund, Private Equity, and Investment Banking experience. Team members are expected to operate with a high degree of ownership and autonomy, contributing directly to core research, product, and modeling decisions while working closely with engineering and senior leadership in a highly collaborative, low-bureaucracy environment with direct access to decision-makers.

• 2+ years of buy-side public markets investing experience, at a hedge fund, long-only fund, family office, or endowment fund that emphasizes long-term investment horizons and independent, fundamental analysis
• Experience and interest in prompt design and context engineering for LLM-based equity research workflows, some experience programming, coding or working with data.
• Experience programming, coding or working with data. You will closely collaborate with the Engineering team to design and build automated equity analysis features blending LLM-based agentic and quanta mental approaches, leveraging a modern and evolving technology stack that is being actively expanded
• Systemic, data driven thinker with the ability to productize fundamental research into a repeatable, systematic strategy and a desire to “build the machine that does the job”
• Experience contributing to long-term investment theses for a sector or single equity supported by qualitative and quantitative factors
• Ability to read financial statements, solid accounting knowledge, experience building three-statements models

• Collaborate to convert equity research insights and conclusions into scalable AI equity research platform features that help individual investors with stock discovery, research, and selection
• Design and evaluate LLM-based workflows to process unstructured data, analyze it and translate it into structured metrics and factors
• Support the development and evaluation of performance of sector-based factor models using a combination of traditional…