Product Manager, Taxonomy
New
This team primarily works remotely and also has an office located in New York City for optional in-office days.Full-TimeMiddle
Salary$111,000 — $139,000 CAD
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Job Details
- Experience
- 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.
- Required Skills
- PythonSQLArtificial IntelligenceProduct ManagementData StructuresLLM
Requirements
- 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.
- At least 2 years in a product management or closely adjacent cross-functional role.
- Genuine interest in financial data and capital markets, with an understanding of how investors and financial professionals consume data.
- Proven analytical mindset, with the ability to translate complex, ambiguous data challenges into structured frameworks.
- Hands-on experience leveraging AI and LLM tools as an internal practitioner.
- Strong organizational skills and attention to detail, with a demonstrated ability to manage multiple workstreams.
- Proficiency in data management tools such as SQL and Python.
Responsibilities
- Own and evolve the existing structured financial data taxonomy library at AlphaSense, inclusive of all client-facing labels, metric definitions and dataset classifications.
- Understand existing naming standards and define how label conventions and governance principles apply across a growing range of data types.
- Collaborate cross-functionally with engineering, data, product, sales, and client-facing teams to align stakeholders on taxonomy decisions.
- Serve as the internal subject matter expert on data semantics, and develop and maintain official internal and external documentation.
- Proactively identify gaps and inconsistencies as new datasets are onboarded.
- Design and maintain taxonomy and data structures with AI and LLM compatibility as a core consideration.
- Lay the foundation for a future taxonomy practice by building repeatable frameworks.
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