Front Office AI Equity Research Analyst

New York City — Hybrid work. Onsite interview required; local candidates only. 6‑month contract‑to‑hire.

About the role

Combines traditional fundamental equity research with artificial intelligence, large language models (LLMs), and quantitative workflows to evaluate public markets, build predictive models, and scale investment insights. The objective is to bridge core fundamental equity analysis (DCF, valuation, earnings analysis) with AI/ML infrastructure to process unstructured data, automate research pipelines, and evaluate LLM performance on financial tasks.

Responsibilities

Fundamental & Quantitative Research

• Conduct deep research and initiate coverage on public equities across various sectors.
• Build and maintain financial models (DCF, comparable company analysis, earnings estimates).
• Design and evaluate sector‑based factor models combining fundamental analysis with statistical/quantitative techniques.

AI & LLM Workflow Integration

• Design, test, and evaluate LLM‑based workflows to process unstructured financial data (SEC filings, transcripts, news) and translate them into structured metrics.
• Create evaluation rubrics and test AI‑powered tools/platforms designed for equity research analysis.
• Partner with engineering teams to build automated data pipelines (APIs, DAGs, databases), run reproducible backtests, and ship AI‑driven research features.

Qualifications

Education

• Bachelor’s or Master’s degree in Finance, Economics, Financial Engineering, Statistics, Econometrics, or Computer Science.

Experience

• 4+ years of practical experience in Equity Research, Credit Research, or a front‑office financial role (M&A, ECM, Buy‑side/Sell‑side).
• Experience evaluating or building LLM/data workflows is increasingly preferred for hybrid roles.

Technical Skills

• Strong mastery of financial statement analysis, valuation (DCF, LBO, comps), and Excel modeling.
• Familiarity with financial databases (Bloomberg, Capital IQ, Refinitiv).
• Understanding of AI/ML concepts, prompt/evaluation frameworks, or scripting/data tools (Python/APIs) preferred depending on the tech stack.