AI Search Data Scientist

AI Search Data Scientist

CiteWorks Studio is hiring an AI Search Data Scientist to convert messy AI visibility signals into reliable metrics, dashboards, benchmarks, and reporting systems for enterprise brands. This technical and analytical role focuses on measuring how brands are understood, retrieved, cited, recommended, compared, and displaced across AI search systems, large language models, generative answer engines, and traditional search environments.

The AI Search Data Scientist will build measurement systems for AI Share of Voice, AI Recommendation Share, citation frequency, brand inclusion rate, prompt-level win/loss analysis, recommendation movement over time, and competitor displacement signals. This is not a traditional marketing analytics role. It sits closer to applied data science, AI search measurement, retrieval analytics, competitive intelligence, and generative engine optimization.

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Overview:

What Is AI Search Data Science

AI search data science is the practice of collecting, cleaning, modeling, and interpreting visibility signals from AI-generated answers, search environments, citation patterns, prompt responses, competitor mentions, recommendation surfaces, review ecosystems, and source attribution behavior.

It helps answer: how often a brand appears, how often competitors are recommended, which prompts include or exclude the brand, which sources are cited most often, which competitors are gaining visibility, whether positioning is accurate, whether reviews influence recommendations, and whether AI visibility is improving, declining, or shifting by category, prompt, region, intent, or model.

AI search data science turns fragmented AI visibility signals into structured metrics to understand performance, identify gaps, and prioritize action.

The Role

What Does an AI Search Data Scientist Do

Builds measurement systems to understand brand visibility across large language models, AI-generated answers, traditional search results, citation environments, and recommendation surfaces.

Measures: AI Share of Voice, AI Recommendation Share, citation frequency, brand inclusion rate, prompt-level win/loss, recommendation movement, competitor displacement signals, source attribution, category trends, model differences, and executive metrics.

Works across data science, AI search visibility, generative engine optimization, information retrieval, marketing intelligence, competitive analysis, analytics engineering, and product strategy—turning AI visibility into a measurable, repeatable system.

About CiteWorks Studio

CiteWorks Studio is a search visibility and AI discovery agency helping brands improve where they rank, are cited, retrieved, and recommended across Google, AI search, large language models, reviews, comparison pages, social platforms, and the public evidence layer.

Buyers now move across Google, ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Microsoft Copilot, YouTube, reviews, Reddit, and social platforms before deciding. CiteWorks Studio studies how models retrieve information, how semantic relationships shape inclusion, how sources influence recommendations, and how citation patterns affect authority.

Work includes AI search visibility strategy, generative engine optimization, semantic vector optimization, citation architecture, SEO and schema strategy, recommendation tracking, review intelligence, and competitive analysis. The company is moving toward SaaS products for scalable AI visibility intelligence.

Role Overview

Design and build measurement systems powering dashboards, reports, benchmarks, and SaaS workflows. Supports AI Share of Voice, AI Recommendation Share, citation tracking, brand inclusion rate, win/loss analysis, recommendation movement, competitor displacement, attribution analysis, model comparisons, and executive dashboards.

Defines how brands are found, cited, recommended, compared, displaced, or ignored by AI systems.

Responsibilities

Build metrics, models, dashboards, and workflows that make AI visibility measurable.

• Develop measurement systems for AI Share of Voice, Recommendation Share, citation frequency, inclusion rate
• Build data models across AI answers, search, prompts, and citations
• Create win/loss analysis systems
• Measure recommendation movement over time
• Identify competitor displacement signals
• Analyze citation and source attribution patterns
• Develop inclusion rate metrics across prompts and models
• Translate data into dashboards, benchmarks, and insights
• Work with product, engineering, SEO, GEO, and strategy teams
• Design data quality processes for noisy AI outputs
• Build reporting frameworks for stakeholders
• Turn raw observations into trackable metrics

Why it Matters

AI-generated answers are dynamic and inconsistent. Brands may appear in one model and disappear in another, be cited in one prompt and ignored in another.

Traditional SEO metrics cannot show recommendation, citation quality, prompt performance, or competitor displacement.

AI search data science creates the measurement layer to track inclusion, recommendation share, citation strength, and movement over time—turning AI visibility into structured intelligence.

Product Area

AI Share of Voice, AI Recommendation Share, Citation Frequency, Brand Inclusion Rate, Prompt-Level Win/Loss Analysis, Recommendation Movement, Competitor Displacement Signals, Source Attribution Analysis, Executive Visibility Reporting.

Qualification

Required:

5+ years in data science, analytics, BI, machine learning, or related fields. Experience building metrics, models, dashboards from messy data. Strong Python, SQL, data modeling, and analysis skills. Ability to define and explain metrics. Experience with noisy and ambiguous data. Understanding of search, SEO, AI systems. Ability to turn data into insights and recommendations

Preferred:

Experience with AI search, LLMs, prompt testing, citation tracking. SaaS analytics, dashboards, or product analytics. Knowledge of semantic search, embeddings, knowledge graphs. Experience in competitive analysis and attribution

Who Will Thrive

A data scientist who thinks like a measurement architect and product builder. Interested in how AI visibility is measured, how recommendations are defined, how competitors displace brands, and how noisy outputs become reliable reporting.

Why Join

This role sits at the center of CiteWorks Studio's SaaS future, building the metrics that define how brands are discovered, cited, recommended, and tracked across AI systems.

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