What the Candidate Will Do ----
- Build predictive models and GTM frameworks — account scoring, forecasting, quota modeling, and Book Building that directly shape where sellers focus and how leadership allocates resources across a global sales org.
- Own high-stakes analytical models — build and maintain outputs where accuracy matters because they directly influence how the business operates and how people are measured, pressure-testing assumptions and surfacing downstream impact before decisions are made.
- Engineer automated data infrastructure — replace manual workflows with scalable SQL pipelines, Python-based automation, and ETL processes so the team scales without bottlenecks.
- Deliver executive insights — produce deep-dive analyses and data products for MBRs, QBRs, and leadership reviews, translating complex data into clear narratives with actionable recommendations.
- Multiply your impact with AI — leverage AI-native tools across your entire workflow to build custom apps, automate repetitive work, and move at startup speed within Uber.
---- Basic Qualifications ----
- More than 4 years of experience in similar roles.
- Strong Experience using AI coding tools (Cursor, Claude Code, Copilot, or similar) as a core part of your workflow.
- Preferably experience with SQL and Python for querying, data transformation, automation, and analytical modeling.
- Demonstrated analytical problem-solving ability—you break down ambiguous problems, think in tradeoffs, and calibrate your rigor to the stakes.
---- Preferred Qualifications ----
- Experience in environments where your analytical output directly affects business operations and people outcomes—and an appreciation for the precision and tradeoff-thinking that responsibility requires.
- Ability to build lightweight data applications (Streamlit, Flask, or similar) that move insights beyond static dashboards into interactive, self-serve products.
- Strong cross-functional communication—you present options with pros, cons, and who bears the impact so leadership makes informed decisions, not just fast ones.
- Background in data analytics, BizOps, consulting, RevOps, or analytics engineering. No specific years required—demonstrated ability and learning agility matter more than tenure.