• Design and develop algorithms and models to optimize Search relevance, retrieval, and ranking • Experiment design: ensure we run experiments efficiently and accurately • Develop and share key strategic insights through data analysis • Build scalable metrics and dashboards to track Search performance • Partner with cross-functional teams to support product development efforts • Care deeply about data quality

• Own the source of truth for GTM health: pipeline, bookings, win rates, sales velocity, rep productivity, GPU consumption trends, and segment performance. • Build and maintain the analytical framework across the full funnel — outbound through AE close and expansion. • Build executive dashboards that give leadership real-time visibility into performance, risk, and opportunity. • Execute annual and quarterly planning cycles: territory design, quota-setting, capacity modeling, and headcount planning in partnership with Finance and the Director. • Build and maintain predictive models for revenue pacing and sales attainment. • Own the quantitative inputs to the forecast: conversion rates, coverage ratios, pipeline aging, and ramp curves. • Build scalable, governed GTM data models in Snowflake and dbt. Own definitional standards: pipeline, logo, ARR, GPU consumption. • Maintain data integrity across Salesforce and GTM systems. One source of truth, enforced. • Deploy AI and automation workflows across GTM analytics: reporting automation, pipeline intelligence, and LLM-assisted analysis. • Automate recurring analytical work so capacity goes to interpretation, not production.

• Data literacy made easy through practical, hands-on training • Flexible, modern online courses in Data Science, Business Analytics and Artificial Intelligence • Discussion of business processes and optimization of decision-making • Building AI models and automating processes

• Dig into experiment results to surface high-impact personalization opportunities; translate what you find into crisp scientific problem formulations that balance rigor with speed-to-learning. • Work closely with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements on explainability and compliance. • Hands-on develop, evaluate, and ship ML models and data pipelines at scale—batch and real-time, structured and unstructured—using Airbnb’s paved-path tooling and AI native mindset • Prototype and iterate quickly: turn a new idea into a working model in a prototype, get early signals from an experiment, then productionize what works. You move fast and don’t wait to be asked. • Present findings and proposals at team reviews and to technical, product, and executive stakeholders—making complex ML results legible without dumbing them down, and generating conviction on the roadmap ahead. • Stay current with the research community; draw on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publish externally or present at conferences to advance Airbnb’s scientific standing.