Product Manager

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

Proficiency in data management tools such as SQL and Python, with a willingness to expand your toolkit as the role evolves

Growth opportunities in a well-funded, high-growth company that’s aiming to be the new fundamental dataset of record .

Autonomy to create your own schedule and do your best work.

We recognize the advantages of working flexibly. This team primarily works remotely and also has an office located in New York City for optional in-office days.

We’re committed to ongoing personal and professional growth and we back it up with a learning stipend, lunch and learns, and guest speakers

We support your development by providing ongoing feedback, career development, and weekly 1-on-1s

We’ll set you up to work remotely and effectively with all the equipment you need

Drive ownership - Own and evolve the existing structured financial data taxonomy library at AlphaSense, inclusive of all client-facing labels, metric definitions and dataset classifications, ensuring both backwards compatibility and future proofing.

Set the standard - Understand existing naming standards and define how label conventions and governance principles apply across a growing range of data types, ensuring clients can intuitively discover, understand, and leverage the data Alpha-Sense has to offer.

Collaborate cross-functionally with engineering, data, product, sales, and client-facing teams to align stakeholders on taxonomy decisions and translate conceptual frameworks into scalable, production-ready systems

Serve as the internal subject matter expert on data semantics, and develop and maintain official internal and external documentation ensuring taxonomy standards are clearly articulated and accessible to all stakeholders

Proactively identify gaps and inconsistencies as new datasets are onboarded, and drive resolution in a structured and scalable way

Design and maintain taxonomy and data structures with AI and LLM compatibility as a core consideration, ensuring labels, definitions, and conventions support model training, inference, and broader AI-driven automation