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AlphaSense is a market intelligence platform trusted by over 6,000 enterprise customers, including a majority of the S&P 500. The company provides AI-driven insights from a comprehensive database of public and private content including equity research, company filings, event transcripts, expert calls, and news.
You will join the Financial Data Team as a Product Manager II specializing in financial data generation and presentation. This role owns and evolves AlphaSense's proprietary financial data taxonomy across sectors, accounting standards, and reporting patterns while ensuring backward compatibility and future-proofing.
Key responsibilities include: defining how financial data generation rules apply across all sectors and data types; serving as the internal subject matter expert on financial data generation and presentation; collaborating cross-functionally with engineering, research, product, sales, and client teams to align on data standards; identifying gaps and inconsistencies as new companies and metrics are onboarded; defining use cases and requirements for new financial datasets beyond core financial statements; designing financial data structures with AI and LLM compatibility as a core consideration; and building repeatable frameworks for scalable financial data expansion.
You will translate complex financial data challenges into structured frameworks, maintain official documentation, and ensure clients can intuitively discover and leverage AlphaSense's data offerings. The role requires deep collaboration across technical and business stakeholders to drive alignment on financial data standards and their implementation.
Required qualifications: Bachelor's degree in an analytical field (Engineering, Computer Science, Business, or related); 5+ years working with data structures, taxonomy, or data governance, with at least 2 years in product management or closely adjacent cross-functional role; genuine interest in financial data and capital markets; proven analytical mindset; hands-on experience with AI and LLM tools; strong organizational skills and attention to detail; proficiency in SQL and Python.
