Job Description

Job Description

About the Role

We are seeking a highly analytical Data Product Analyst to help evaluate, improve, and evolve data products. This role sits at the intersection of data analysis, product thinking, and user-facing data systems.

We are a small, fast-moving startup , and this role offers a rare opportunity to get in early and have a meaningful impact on our product. You will work closely with a small team, take ownership of key areas, and help shape processes, standards, and resources from the ground up.

Our vision is a world where access to data is no longer a constraint on research . Achieving this requires data is understandable, usable, and trusted. In this role, you will work closely with internal data teams, users, and external data providers to ensure datasets are reliable, well-documented, and aligned with real-world use cases. User questions and feedback will serve as a key signal to help you identify gaps in data quality, documentation, or tooling and translate those insights into concrete product improvements.

This role is ideal for someone who enjoys digging into complex datasets, and improving how data products are designed, documented, and delivered.

Key Responsibilities

Data Product Evaluation & Improvement

• Evaluate datasets for structure, quality, completeness, and usability from a user perspective.
• Identify recurring friction points or sources of confusion and translate them into actionable improvements.
• Partner with internal teams to influence dataset standards, documentation practices, and release readiness.
• Contribute to best practices for dataset onboarding, versioning, and lifecycle management.

Data Analysis & Validation

• Use SQL, Python, and R to explore, validate, and diagnose issues in datasets.
• Identify inconsistencies, edge cases, or limitations and surface clear, actionable recommendations.
• Perform reproducible analyses to validate assumptions and resolve open questions.

User Feedback & Signal Gathering

• Engage with user questions and feedback as an input into data product performance.
• Investigate issues independently through documentation, metadata, and exploratory analysis.
• Escalate well-framed, high-impact findings to internal stakeholders or external providers when necessary.

Documentation & Resource Development

• Create and maintain high-quality resources such as codebooks, data dictionaries, tutorials, examples, and usage guides.
• Improve clarity around dataset assumptions, limitations, and appropriate use cases.
• Develop scalable documentation patterns that reduce future ambiguity and support self-service usage.

Domain Insight & Contextual Understanding

• Develop an understanding of how different user groups interact with data products and adapt resources accordingly.
• Monitor usage patterns and feedback to propose forward-looking improvements.

Provider & Partner Collaboration

• Communicate with external data providers to resolve issues that cannot be addressed through internal analysis.
• Track open questions and resolutions to inform future data product enhancements.
• Advocate for user needs with clear, professional, and evidence-backed communication.

Qualifications

• Experience working with large or complex datasets in analytics, data product, research, or engineering-adjacent roles.
• Strong proficiency in SQL, Python, and R for exploratory, validation, or diagnostic analysis.
• Strong written and verbal communication skills, especially in explaining complex data topics clearly.
• Strong organizational skills and attention to detail; ability to manage multiple datasets and workstreams simultaneously.

Nice to Have

• Experience working with academic or research-oriented users.
• Familiarity with literature review practices or research workflows.
• Experience creating or maintaining structured documentation for data products or technical tools.
• Exposure to regulated or methodologically complex domains (e.g., economics, finance, public policy).

What We're Looking For

• A product-minded analyst who views questions and issues as opportunities to improve systems.
• A strong investigator who can move fluidly between documentation and raw data.
• A clear communicator who can translate technical findings into user-facing insights.
• An owner who wants to help shape how data products mature over time.

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