AI Search Analyst

Scrunch•New York, NY

19h•Hybrid

About The Position

About Scrunch Scrunch , a venture-backed startup, is on a mission to bring brands to an AI-first future—where people increasingly rely on LLMs to discover, understand, and act on information that matters to them. As AI search and conversational agents replace traditional web search and browsing, Scrunch helps marketing teams rethink how their products and services are discovered and surfaced on AI platforms like ChatGPT, Claude, Gemini, and more—working with AI platforms, not against them. This shift represents the biggest change to marketing since the dawn of the internet. With $26M in backing from Mayfield Fund, Decibel, Homebrew, GTM Capital, and leading Silicon Valley founders and operators, Scrunch has scaled rapidly since commercial launch. Today, more than 500 paying brands—including Fortune 500 companies like Lenovo, category-defining brands like Skims, and breakout startups like Clerk—use the platform. About the Role We’re looking for an Analyst to help us measure, explain, and improve brand visibility across AI-powered search and discovery surfaces. This is a quantitative, customer-facing role for someone who is strong in analysis, measurement, experimentation, and storytelling. You’ll work closely with Customers, Customer Success, Product, Engineering, and Marketing to turn ambiguous questions into clear analyses, practical recommendations, and repeatable measurement. A big part of the job is helping customers understand what is happening in AI search, why it is happening, and what they should do next. Location Requirement: This role is based in New York City and is considered Hybrid, 3x/week in-office with flexibility.

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 .