Product Manager II, Financial Data

The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content.

The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us!

Alpha-Sense is the leading market intelligence platform for key decision makers. We power some of the world’s most well-respected institutional investors, corporations, and consultancies through the largest and most comprehensive database of primary and market information. Our products and services enable clients to discover unmatched insights and answers to the most challenging questions they face to help them make better informed decisions.

Join the Financial Data Team at AlphaSense as a Product Manager specializing in Financial Data Generation & Presentation. Successful candidates will bring a strong analytical mindset, proficiency in financial reporting and structured financial data, and the ability to drive alignment across technical and business stakeholders.

• →Own and evolve AlphaSense's proprietary financial data taxonomy, inclusive of financial metrics reported across sectors, accounting standards, statements, and reporting patterns, ensuring both backwards compatibility and future proofing.
• →Understand financial data consumption patterns of capital markets participants and define how data generation rules apply across all sectors and data types, ensuring clients can intuitively discover, understand, and leverage the data AlphaSense has to offer.
• →Serve as the internal subject matter expert on financial data generation and presentation, and develop and maintain official internal and external documentation ensuring data standards are clearly articulated and accessible to all stakeholders.
• →Collaborate cross-functionally with engineering, research, product, sales, and client-facing teams to align stakeholders on financial data generation and presentation rules and translate them into a dataset with depth, accuracy, and relevance to clients.
• →Proactively identify gaps and inconsistencies as new companies, metrics, and datasets are onboarded, driving efficient resolution in coordination with relevant stakeholders.
• →Define the use cases and requirements for new financial datasets that extend beyond the three core financial statements, driving their onboarding in a structured and scalable way.
• →Design and maintain financial data structures and taxonomy with AI and LLM compatibility as a core consideration, ensuring labels, definitions, and conventions support model training, inference, and broader AI-driven automation.
• →Lay the foundation for financial data expansion through building repeatable frameworks and processes, while positioning the dataset for scalable accuracy, relevance, and growth.

• Bachelor's degree in a highly analytical field such as Engineering, Computer Science, Business, or a related discipline
• 5+ years of experience working with data structures, taxonomy, or data governance, with at least 2 years in a product management or closely adjacent cross-functional role
• A genuine interest in financial data and capital markets, with an understanding of how investors and financial professionals consume, interpret, and act on data
• A proven analytical mindset, with the ability to translate complex, ambiguous data challenges into structured frameworks and scalable solutions
• Hands-on experience leveraging AI and LLM tools as an internal practitioner and a genuine curiosity for how consumers of financial data are integrating AI into their workflows
• Strong organizational skills and attention to detail, with a demonstrated ability to manage multiple workstreams and competing priorities independently
• Proficiency in data management tools such as SQL and Python, with a willingness to expand your toolkit as the role evolves