Data Researcher

Minimum Qualifications

Documentation: Maintains clear, structured documentation of project logic, decisions, and maintenance. Contributes to team standards for reproducibility and transparency.

Collaboration: Works effectively with cross-functional partners. Values shared ownership and ensures continuity through knowledge sharing.

Initiative: Comfortable in ambiguity; proactively identifies issues and opportunities. Demonstrates curiosity and critical thinking.

Attention to Detail: Delivers high-quality, consistent code and documentation that supports long-term maintainability and trust in data systems.

3+ years of hands-on experience designing controlled experiments — forming hypotheses, holding conditions constant while varying one thing at a time, establishing baselines, and designing against bias.

1+ year of hands-on experience constructing AI agents and using AI for analytics — prompt engineering, model/parameter control (temperature, etc.), programmatic API access, retrieval/RAG, and agent architecture. Experienced enough to modify these components confidently, because they are the variables you’ll be manipulating.

5+ years of data science fundamentals, including Python, SQL, and Snowflake; ability to reason about metrics and define what a “correct answer” to an analytical question actually is.

3+ years of experience evaluating model / ML system performance — measuring error and variability rigorously (percent error, distributions, reproducibility) and reasoning about failure modes.

5+ years maintaining disciplined, reproducible documentation — structured logging and records another person could re-run.