Responsibilities
- Build and maintain analyses, dashboards, and reporting that track AI search performance, including visibility, citations, competitive movement, prompt/topic performance, and content coverage.
- Translate platform data into clear customer-facing insights and recommendations tied to business goals, customer journeys, personas, and content priorities.
- Partner with Customer Success on client working sessions, reviews, investigations, and strategic readouts.
- Diagnose performance changes by analyzing prompt patterns, topic mix, source behavior, competitor activity, and platform shifts across AI surfaces.
- Design and support measurement frameworks and experiments, including hypothesis development, KPI definition, pre/post analysis, and readouts.
- Conduct quantitative deep dives into questions like what drives citations, where visibility is being won or lost, and how content or technical changes affect outcomes.
- Help improve internal methodologies for trend reporting, benchmarking, segmentation, and performance interpretation in noisy, fast-changing environments.
- Partner with Product and Engineering to surface customer pain points, edge cases, and opportunities for better reporting, analytics, and workflows.
- Contribute to external-facing analysis where useful, including research summaries, benchmark studies, and thought leadership content.
Requirements
- 2–4 years of experience in analytics, data science, consulting, or a similarly quantitative role.
- Strong foundations in statistics, analysis, and measurement.
- Experience working with messy, high-volume behavioral or product data and turning it into usable insights.
- Strong SQL and proficiency in Python for analysis.
- Experience building dashboards, analyses, or data products that inform decisions for customers or business stakeholders.
- Ability to structure ambiguous problems, choose sensible metrics, and explain what you found, why it matters, and how confident you are.
- Comfort working with noisy, incomplete, or non-representative data, including bias checks, caveats, and uncertainty-aware interpretation.
- Strong communication skills and comfort in customer-facing settings: you can present findings, answer questions live, and adapt technical detail to the audience.
Nice to Have
- Experience in digital analytics, SEO, search, content strategy, marketing analytics, or experimentation.
- Familiarity with AI search / LLM platforms and how visibility differs from traditional search.
- Experience with competitive analysis, benchmarking, or performance reporting.
- Exposure to NLP or classification workflows, even if you were not the primary model builder.
- Experience supporting enterprise customers in a strategic, analytical, or solutions-oriented role.
- Familiarity with the web as a system, including content structure, domains, crawlability, and measurement constraints.
- Familiarity with schema.org, structured content, technical SEO, or content operations.
Benefits
- Equity in a fast-growing, category-defining company
- Medical, dental, vision, and life & disability insurance
- Paid parental leave when life's biggest moments happen
- Home office stipend so your workspace doesn't suck
- Phone and internet reimbursement
- L&D budget for courses, conferences, and whatever makes you sharper
- Flexible PTO — take what you need, we trust you
- 401(k)
- Team offsites and a crew that genuinely likes each other
Work Arrangement
Hybrid
Additional Information
- 3x/week in-office with flexibility.
