Senior Data Scientist / Data Engineer Generative AI Engine Optimization

Senior Data Scientist / Data Engineer Generative AI Engine Optimization

Job Summary

We are seeking a Senior Data Scientist / Data Engineer to own the complete data pipeline infrastructure, metric governance, warehouse architecture, executive dashboards, and AI-access layer for our Generative Engine Optimization (GEO) initiative. As consumer discovery shifts toward generative AI environments such as ChatGPT, Google AI Overviews & AI Mode, Gemini, Claude, and Perplexity this role ensures our core products remain recommended, favorability-ranked, and accurately represented.

This position bridges production data engineering and advanced statistical analysis. The role starts build-heavy in the first 3 months (focusing on data pipeline ingestion, warehouse materialization, metric governance, and custom LLM connectors) and transitions into a steady state focused on deep-dive analytical research, AI enablement, and executive thought leadership.

Important areas
Data Pipelines & Database
Metric Layer & Governance
Dashboards & Reporting
Data Access & AI Enablement
Deep-Dive Analysis & Thought Leadership

Key Responsibilities

• Data Pipeline Engineering: Ingest AI-answer tracking data from vendor APIs, Google Search Console (via BigQuery), crawler logs, and intervention registries into the enterprise data warehouse (Databricks / Athena) with data quality monitoring and lineage documentation.
• Metric Layer & Governance: Materialize key performance metrics including Recommendation Win Rate, presence, favorability, share of voice, owned citation share, and answer accuracy into versioned, governed warehouse views so all teams compute them identically.
• Executive Dashboards: Build and own the portfolio dashboard across all core consumer products, featuring north-star trends, competitor benchmarks, platform splits, and automated weekly reads with noise flags in Tableau, Looker, or similar BI tools.
• AI Tooling & Context Layer: Design a Model Context Protocol (MCP) or plugin connector over governed warehouse tables, paired with metric definitions, provenance documentation, and a 20-question evaluation set so stakeholders can query GEO metrics via Claude, ChatGPT, or Gemini without hallucinations.
• Experimental Infrastructure: Stand up an answer-accuracy classifier to evaluate unstructured LLM outputs and deploy a controlled prompt runner on model APIs (web search toggles, model pinning, raw answer capture) for custom experimentations.

Analytics & Thought Leadership Phase

• Advanced Deep-Dive Analytics: Conduct rigorous analyses to investigate organic search interception, citation drivers, retrieval vs. content index issues, and model memory update speeds.
• AI Enablement & Maintenance: Maintain data pipelines, onboard new data sources, expand self-serve AI query access, and continuously verify LLM assistant outputs.
• Executive Thought Leadership: Communicate testable recommendations and strategic insights through concise memos and updates directly to marketing leadership.

Required Qualifications & Must-Have Skills

• Education & Experience: Bachelor s degree in Computer Science, Statistics, Mathematics, Economics, Engineering, or a quantitative field (Master s degree preferred). 5+ years of combined data engineering and data science experience owning production-grade data products end-to-end.
• Advanced SQL & Python: Master-level SQL (Presto/Athena, BigQuery, Databricks SQL) and Python (pandas, API integration, pipeline orchestration/scheduling). Proven ownership of data ingestion, schema design, lineage, and monitoring.
• Applied Data Science & Experimental Rigor: Strong judgment in experimental design, noise and seasonality handling, difference-in-differences analysis, and honest uncertainty communication.
• Executive BI & Dashboards: Demonstrated success creating executive-facing dashboards in Tableau, Looker, or equivalent backed by clean metric governance.
• LLM Tooling & Evaluation: Hands-on experience configuring LLM connectors, plugins, or MCP tools, authoring context documentation/guardrails for AI models, and building evaluators/classifiers over unstructured text.
• Communication: Exceptional written communication skills for delivering brief executive memos and strategic reads to non-technical leadership.

Preferred Qualifications

• GEO / AEO / SEO Measurement: Familiarity with Google Search Console, crawler logs, AI-visibility tracking tools, and AI retrieval mechanics (e.g., how AI Overviews, ChatGPT Search, and Perplexity cite sources).
• Semantic Layer & Data Cataloging: Experience with metric stores, dbt metrics, and governed self-serve analytics.
• Text Classification: Experience building classifiers over unstructured text content.
• Marketing Analytics: Exposure to attribution modeling, Media Mix Modeling (MMM) literacy, and incrementality testing.
• Domain Experience: Background in consumer FinTech or tax software platforms.

Generative Engine Optimization Engineer, GEO Data Scientist, Senior Data Scientist, Data Engineer, Analytics Engineer, AI Measurement Engineer, AEO Data Scientist, LLM Analytics Engineer, Senior Data Engineer

SQL, Databricks, Databricks SQL, AWS Athena, Presto, Google BigQuery, Python, pandas, REST APIs, Pipeline Orchestration, Tableau, Looker, Looker Studio, Model Context Protocol, MCP, LLM Plugins, dbt, dbt Metrics, Semantic Layer, Google Search Console, AI Visibility Tools, Git, Claude, ChatGPT, Gemini, Perplexity, OpenAI API

Generative Engine Optimization, GEO, Answer Engine Optimization, AEO, Search Engine Optimization, SEO, Data Engineering, ETL Pipelines, Data Warehousing, Metric Governance, Executive Dashboards, LLM Evaluation, LLM Classifiers, Experimental Design, Difference-in-Differences, Noise Reduction, Seasonality Modeling, AI Visibility Tracking, Citation Share, Recommendation Win Rate, Text Classification, Natural Language Processing, NLP, Marketing Analytics, Attribution, Incrementality, FinTech, Consumer Platforms