Associate, Investment Data Science (Investment Researcher)

Introduction

The Investment Researcher will harness the power of alternative data to generate differentiated investment insights at the single-stock, sector, and, where relevant, macro level. Relevant alternative datasets may include sources such as web traffic, mobile app usage, and natural language data. The role is centered on applying these datasets selectively and rigorously to sharpen fundamental research, strengthen investment judgment, and deepen understanding of companies, industries, and market dynamics.

The Investment Researcher will serve as a bridge between our sector and market investment teams and our Investment Data Science teams. This individual will help embed AI, advanced analytics, and innovative research workflows into the investment process, combining deep dataset knowledge with strong fundamental investing instincts to address high-value research questions.

Responsibilities

Apply AI and advanced analytics to fundamental research and investment decisions

• The Investment Researcher will work closely with investment teams to frame and answer investment-relevant questions through the use of alternative data, AI-enabled research techniques, and advanced analytical methods. The role requires translating investment questions into practical analytical approaches, identifying the most relevant datasets and tools, and generating insights that can inform company, sector, and market views. A strong emphasis will be placed on supporting fundamental research, strengthening conviction, and improving the quality and speed of investment decision-making.

Drive AI-centric research workflows and dataset application

• The Investment Researcher will develop a deep understanding of individual datasets, including where they are most useful, how they should be interpreted, and the limitations and trade-offs associated with each. Beyond conducting targeted analysis, this role will help design and refine AI-native workflows that improve how investors source information, analyze companies, test hypotheses, and synthesize insights. Success in the role will depend on strong dataset intuition, sound analytical judgment, and a practical understanding of how AI can enhance the fundamental research process in scalable and repeatable ways.

Partner with external data suppliers and solution providers

• The Investment Researcher will engage external suppliers and service providers on data sourcing, dataset evaluation, and related analytical applications. This includes assessing the relevance, quality, and practical usability of external datasets, and helping ensure they are deployed effectively in support of investment research objectives.

Requirements

• Minimum 2 years of relevant experience, preferably in private equity, public equities, equity research, investment research, investment banking, corporate finance, or management consulting
• Strong foundation in corporate finance and accounting
• Demonstrated experience in financial statement analysis, returns analysis, valuation analysis, and company and industry research
• Strong research and analytical skills, with the ability to connect data-driven work directly to an investment thesis and to core underwriting questions
• AI-native mindset with familiarity across a range of AI tools, workflows, and use cases, including a clear understanding of their strengths, limitations, and trade-offs
• Ability to apply AI and advanced analytics in a practical manner to improve research efficiency, insight generation, hypothesis testing, and investment decision support
• Experience working with alternative data sources is preferred; experience applying such data in an investing context would be a strong advantage
• Strong written and verbal communication skills, with the ability to communicate analytical insights clearly and influence investment discussions
• Working knowledge of Python and SQL is preferred, though the primary emphasis is on analytical judgment, research application, and effective use of AI-enabled workflows
• Strong interpersonal skills and the ability to build trust and credibility with investment teams, data science partners, and external providers
• Ability to work independently, navigate ambiguity, and rapidly build understanding across new companies, sectors, and datasets