Role: AI Analyst
About the job
About Millennium
Millennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.
Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.
About The Role
The Equity Management Team is seeking an AI Analyst to work directly with portfolio managers, fundamental equity analysts, investment professionals and technology partners. This newly created role will focus on identifying, designing, implementing, and scaling high-value AI applications across the fundamental equity investment process.
The successful candidate will combine deep buy-side fundamental equity-research experience with strong technical knowledge of modern AI systems. They will bring practical judgment about where AI can materially improve research quality, speed, breadth, and decision-making—and where investment judgment and conventional analytical approaches should remain central.
Core Responsibilities
- Translate fundamental equity-research needs into clearly defined AI use cases, measurable objectives, and practical technical solutions.
- Partner with portfolio managers and analysts to improve workflows across company research, industry analysis, earnings preparation, document analysis, knowledge retrieval, data extraction, monitoring, and idea generation.
- Evaluate and help implement AI-enabled research workflows, including large-language-model applications, retrieval-augmented generation, agentic workflows, document intelligence, structured-data extraction, and internal knowledge systems.
- Assess AI models, vendors, platforms, and tools for relevance to fundamental equity investing, considering accuracy, robustness, security, usability, scalability, latency, and cost.
- Develop rigorous frameworks to evaluate AI outputs, including benchmarking, source attribution, factual accuracy, hallucination risk, workflow reliability, user adoption, and investment-process impact.
- Work closely with engineers, data teams, and technology stakeholders to translate investment requirements into deployable tools, APIs, data pipelines, and monitored applications.
- Establish practical standards for responsible AI use in investment research, including confidentiality, data governance, model-risk controls, appropriate human oversight, and documentation.
- Identify opportunities to improve the efficiency and consistency of research processes while preserving the quality of investment judgment and differentiated fundamental insight.
- Track relevant developments in AI, machine learning, data infrastructure, and investment technology, and translate the most relevant advances into durable capabilities for the Equity Management Team.
Qualifications / Skills Required
- Minimum 5 years of buy-side experience as a fundamental equity analyst.
- Strong understanding of the end-to-end fundamental equity investment process, including idea generation, research, earnings analysis, catalysts, portfolio-management considerations, and risk.
- Deep practical knowledge of modern AI implementation and its applications in fundamental equity investing.
- Strong understanding of large language models, prompting, retrieval-augmented generation, embeddings, vector search, AI agents, document processing, structured-data extraction, and model evaluation.
- Ability to assess AI systems critically, including their limitations, hallucination risk, factual reliability, privacy and confidentiality considerations, security, and governance requirements.
- Experience translating business or investment workflows into technical requirements and collaborating effectively with engineers, data scientists, and technology teams.
- Strong analytical judgment and the ability to distinguish high-value, scalable AI use cases from applications that do not justify their complexity or risk.
- Excellent written and verbal communication skills, with the ability to explain technical concepts clearly to investment and senior-management audiences.
- Professional fluency in English and Mandarin.
Preferred Qualifications
- Experience implementing AI, data, or technology products in a hedge fund or a similarly research-intensive environment.
- Hands-on proficiency with Python, SQL, APIs, data platforms, or AI-development frameworks.
- Experience evaluating and deploying LLM-enabled applications, retrieval systems, research copilots, agentic workflows, and enterprise knowledge-management tools.
- Experience working with structured and unstructured financial data, including filings, earnings-call transcripts, research documents, market data, alternative data, and news.
- Familiarity with data pipelines, cloud infrastructure, model monitoring, user-feedback loops, and production-quality application delivery.
- Experience establishing AI evaluation, testing, governance, or model-risk frameworks.
- Genuine intellectual curiosity about companies, industries, investing, and the evolving AI ecosystem.
- A practical, collaborative working style characterized by ownership, scientific rigor, clear communication, sound judgment, and a focus on delivering measurable value for investment professionals.