Research Analyst (Markets)

• Strong analytical mindset and high attention to detail.
• Comfortable working under time pressure with incomplete information.
• Clear written communication, structured thinking, and ability to prioritise signal over noise.
• Good judgment and common sense in messy, real-world situations.
• Quantitative fluency (basic probability, compounding, scenario analysis).Strong interest in financial markets and corporate actions (M&A, IPOs)
• Strong academic performance.

• Prior exposure to research, investing, or event-driven market analysis (internship, coursework, personal projects).
• Experience with Python, Excel/Sheets, or similar analytical tools.
• Familiarity with regulatory filings and primary-source research.
• Additional languages, especially European languages.

At Intropic, we're building the future of financial intelligence, where deep market expertise meets the power of AI. Founded in London's financial centre of Canary Wharf, we exist to transform complex data into clarity, precision, and action. Our culture is shaped by truth-seeking, velocity, and ownership, values that drive how we build, learn, and collaborate every day. We move fast, think independently, and hold ourselves to the highest standards of integrity and impact. Here, curiosity isn't just encouraged, it's essential. If you're driven by challenge, inspired by innovation, and ready to amplify your intelligence alongside a team of exceptional thinkers, Intropic is where your ideas can truly compound.

• Track and analyse market-moving corporate events (M&A, IPOs) and public company news.
• Extract key information from primary public sources (company announcements, regulatory filings, investor materials) and summarise it accurately.
• Build clean, auditable calculations and simple scenarios to frame risk and outcomes.
• Write concise research notes for a professional audience, focused on what matters and what could change.
• Validate outputs from internal tools and workflows, flagging errors, edge cases, and missing assumptions.
• Contribute to ongoing improvement of research processes, templates, and data quality.