Data Product Manager – Super Agent

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of transforming raw alternative datasets into scalable, agent-ready data products.

• Create methodologies that address high-value business inquiries, determining the best ways to combine, normalize, and interpret varied datasets.

• Collaborate closely with Data Engineering to convert source data pipelines into clean, well-structured datasets with clear definitions and thorough documentation.

• Cultivate in-depth knowledge of the strengths, limitations, biases, and coverage characteristics of key datasets, ensuring these nuances are reflected in downstream outputs.

• Specify how the Insight Agent should utilize different datasets, including valid query patterns, edge cases, potential failure modes, and methodological guidelines.

• Manage metric definitions, data lineage, and documentation to guarantee the agent consistently provides accurate and explainable responses.

• Set standards for how the agent interprets multiple datasets, avoiding over-interpretation and ensuring that conclusions remain statistically defensible.

• Act as the final reviewer for any methodology-related changes that impact agent behavior.

• Convert customer inquiries into scalable methodologies, data models, and agent capabilities.

• Broaden the range of inquiries the agent can address by enabling new forms of segmentation, cohort analysis, behavioral measurement, and cross-dataset insights.

• Collaborate with Product, Engineering, and Leadership to identify new data sources, use cases, and capabilities that enhance the commercial value of the Insight Agent.

• Contribute to shaping the product roadmap by translating emerging customer needs and experimental insights into repeatable product functionality.

• Oversee testing and validation of staged data modifications before they are deployed to production.

• Manage incident processes related to data quality issues, methodology changes, and upstream source disruptions.

• Create and maintain a library of quality checks tailored to the specific requirements of AI-powered customer experiences.

• Ensure the agent consistently delivers reliable, accurate, and internally consistent information across all supported use cases.

⛳️ Requirements

• 3–6 years of experience in data product management, product analytics, analytics engineering, data science, market intelligence, alternative data, or a related field.

• Strong proficiency in SQL; familiarity with data pipelines, schema modifications, and upstream/downstream data dependencies.

• Experience managing data documentation, metric definitions, or data quality programs—not merely conducting ad hoc analysis.

• A proven history of cross-functional coordination, ideally between technical data teams and product or commercial stakeholders.

• Robust project management skills: able to run a triage process, maintain a quality library, and coordinate across multiple stakeholder groups without oversight.

• Clear and structured communication skills—you can translate complex data methodology inquiries into actionable guidance for non-technical stakeholders.

• A demonstrated record of accomplishment in building—delivering products, solving complex problems, and making a significant impact on the products you’ve developed.

• An entrepreneurial mindset: comfortable with uncertainty, motivated by new challenges, and able to make progress without a fully paved path.

• Extensive experience with alternative data, panel data, or similarly complex data sources is essential—you must understand the nuances, limitations, and methodological subtleties of these datasets and be able to encode that understanding for an AI agent.

• Previous experience with AI/ML products, LLM-based agents, or evaluation frameworks is a significant advantage.

🏝️ Benefits

• Competitive base salary accompanied by comprehensive benefits.

• Fully remote-friendly position available within the United States.

• Flexible working hours and vacation policies.

• Generous 401(k) matching, parental leave, wellness budget, and learning reimbursement.

• A growth-oriented environment where career advancement is based on impact rather than tenure.