Senior Data Analyst

• 5+ years of experience in analytics, data science, research, and/or search products \n

• 2+ years of experience with AI search, SEO/GEO/AEO, content performance, or LLM evaluation \n

• Hands-on fluency with LLMs + agentic tooling - prompt design, evaluation harnesses, agents that collect and process data at volume \n

• Strong SQL and Python skills - experience with Databricks is preferred (or similar tools) \n

• Proven experience in experiment design applied to observational data - comparison group construction, difference-in-differences or matched comparison, and honest treatment of statistical power \n

• Strong analytical judgement with unstructured text: designing rubrics, coding qualitative signals into structured data, and reasoning carefully about ambiguous cases \n

• Startup experience is required - fast-moving, small teams, high visibility/ownership \n

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• Tracking how often, how favorably, and how accurately clients appear in AI answers versus a defined competitive set - with rigorous baselines, sampling, and stated uncertainty so real change can be distinguished from noise. \n

• Maintaining the tracked-prompt panels (across engines, topics, markets) this runs on, and report movement over time as the core output. \n

• Identifying which sources AI systems cite for a given topic and characterize that mix \n

• Tracking whether and how published content later surfaces as cited evidence, how long that takes, and how long it lasts - surfacing where a client’s evidence base is thin and what’s missing \n

• Testing which content traits (source, framing, format, specificity, recency, structure) make material more likely to get picked up by AI systems. \n

• Connecting AI visibility to real outcomes - referral traffic, engagement, bookings/conversions and build an honest instrumentation view of what’s measurable today, what isn’t, and where the recommendation-to-revenue chain breaks \n

• Evaluating AI answers for accuracy, relevance, and completeness; catalog failure modes by engine over time; maintaining the rubrics/benchmarks/rater-agreement process \n

• Continuously refining how prompts are constructed - applying the same evaluation discipline to any AI features built internally \n

• Staying ahead of how AI search systems retrieve/cite sources, validating claims via research (not industry commentary), defining product metrics with product/engineering, ensuring every customer-facing figure is fully re-derivable from documented inputs \n

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• 401K with 4% match, monthly wellness stipend, comprehensive medical and dental insurance plans, long-term disability, and more