AI Search Analyst

Scrunch•New York, NY

3d•Hybrid

About The Position

Scrunch is a venture-backed startup focused on guiding brands into an AI-first future, where LLMs are increasingly used for information discovery and action. As AI search and conversational agents evolve, Scrunch assists marketing teams in adapting their product and service discovery strategies for AI platforms like ChatGPT, Claude, and Gemini. This represents a significant transformation in marketing, comparable to the advent of the internet. With substantial backing from prominent investors, Scrunch has experienced rapid growth since its commercial launch, now serving over 500 paying brands, including Fortune 500 companies, category leaders, and emerging startups. This role is a quantitative, customer-facing position for an individual skilled in analysis, measurement, experimentation, and communication. The Analyst will collaborate with Customers, Customer Success, Product, Engineering, and Marketing teams to transform complex questions into clear analyses, actionable recommendations, and reproducible measurements. A key aspect of the role involves educating customers on the dynamics of AI search, explaining the underlying causes, and guiding their next steps.

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 Haves

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.

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.