The opportunity
The Enterprise Analytics team builds the trusted data products and insights behind GitLab’s Company wide sales, revenue, and finance reporting. We work across the business on metrics including First Orders, pipeline, CRR, ARR, and other key indicators, helping leaders make…
What you'll do
Embed yourself with business teams to become a trusted partner supporting: insight generation and strategic decision making across GTM.
Perform exploratory data analysis to uncover behavioral trends, long-term: patterns, and the key drivers and levers behind business performance, and communicate their impact clearly to non-technical audiences.
Build and productionize machine learning models: such as propensity, product-qualified lead (PQL) scoring, churn, and expansion models - including training, validation, testing, and feature engineering, using MLOps practices and tooling (CI/CD for ML pipelines, model registries, automated retraining, and monitoring) to move work reliably from notebook to production
Design, run, and evaluate growth experiments (A/B tests and: quasi-experiments) on onboarding, pricing and packaging, and upgrade flows, and measure their impact on activation, conversion, and retention
Build and operationalize agentic workflows for analysis, modeling, and: pipeline automation, with evaluation and monitoring that keep agent-generated insights accurate, traceable, and trustworthy.
Contribute to GitLab’s Semantic Layer enabling stakeholders and agents to: self-serve metrics using trusts and tested sources.
What they're looking for
- Write production-grade Python and SQL that meets internal standards for: style, maintainability, and performance in a high-scale database environment; maintain and advocate for these standards through code review
