Job Description
Product · New York, NY (Remote-Friendly) About the Role YipitData is making one of its most ambitious Product bets: building an AI-powered product that transforms how clients interact with data. This initiative sits at the center of our product strategy and represents a fundamentally new way for customers to access and derive value from our data. As a Data Product Manager, you will play a pivotal role in making that vision a reality. You will own the path from raw alternative data to trusted, product ready intelligence—determining how complex datasets are structured, interpreted, and ultimately surfaced to customers. This role sits at the intersection of data, product, and AI. You will work with diverse alternative datasets and develop the methodologies that transform those signals into reliable business insights. You will also serve as a key authority on how the product uses data, helping define what conclusions are methodologically sound, what questions can be answered confidently, and where appropriate guardrails should exist. Success in this role requires both analytical rigor and a builder's mindset. You'll thrive in ambiguity, tackle problems without established playbooks, and help shape the future of one of YipitData's most strategic products. Your work will directly influence how hundreds of customers interact with our data and how this product scales over time. What You'll Do Data Source Ownership & Methodology Design Own the translation of raw alternative datasets into scalable, AIready data products. Design methodologies that answer high-value business questions, determining how disparate datasets should be combined, normalized, and interpreted. Partner closely with Data Engineering to shape source data pipelines into clean, well-structured datasets with clear definitions and documentation. Develop deep expertise in the strengths, limitations, biases, and coverage characteristics of key datasets and ensure those nuances are reflected in downstream outputs. AI Product Intelligence & Knowledge Systems Define how the product should use different datasets, including valid query patterns, edge cases, failure modes, and methodological guardrails. Own metric definitions, data lineage, and documentation to ensure the product consistently delivers accurate and explainable answers. Establish standards for how the product reasons across multiple datasets, preventing over-interpretation and ensuring conclusions remain statistically defensible. Serve as the final reviewer for methodology-related changes that impact product behavior. Product Development & Customer Problem Solving Translate customer questions into scalable methodologies, data models, and product capabilities. Expand the range of questions the product can answer by enabling new forms of segmentation, cohort analysis, behavioral measurement, and cross-dataset insights. Partner with Product, Engineering, and Leadership to identify new data sources, use cases, and capabilities that increase the commercial value of the AI product. Help shape the product roadmap by turning emerging customer needs and experimental insights into repeatable product functionality. Data Quality & Operational Excellence Coordinate testing and validation of staged data changes before they reach production. Own incident management processes for data quality issues, methodology changes, and upstream source disruptions. Build and maintain a library of quality checks tailored to the unique requirements of AI-powered customer experiences. Ensure the product consistently surfaces reliable, accurate, and internally consistent information across all supported use cases. You Are Likely to Succeed If You Have 3–6 years of experience in data product management, product analytics, analytics engineering, data science, market intelligence, alternative data, or a closely related field. Strong fluency in SQL; comfort with data pipelines, schema changes, and upstream/downstream data dependencies. Experience owning data documentation, metric definitions, or data quality programs—not just conducting ad hoc analysis. A track record of cross-functional coordination, ideally between technical data teams and product or commercial stakeholders. Strong project management instincts: you can run a triage process, maintain a quality library, and coordinate across multiple stakeholder groups without dropping balls. Clear, structured communication—you can translate complex data methodology questions into guidance that non-technical stakeholders can act on. A demonstrable track record of building—shipping things, solving hard problems, and leaving a clear mark on the products you’ve worked on. An entrepreneurial mindset: you’re comfortable with ambiguity, energized by new problem spaces, and don’t need a fully paved road to make progress. Deep experience with alternative data, panel data, or similarly complex, nuanced data sources is required—you need to understand the quirks, limitations
