Data Scientist, Enterprise Analytics — GTM & ML

Royper Royco LLC; United States; Remote; USD 86,000 - 176,000; Full time

Benefits offered by this job

Equity Compensation & Employee Stock-P

Growth and Development Fund

Parental Leave

Flexible Paid Time Off

Job summary

GitLab is seeking a Data Scientist for the Enterprise Analytics team to forecast go-to-market performance and size opportunities. You will apply statistics, experimentation, and machine learning to drive activation, conversion, expansion, and retention across GTM metrics.

You’ll embed with Sales, Revenue Operations, and Finance, build production-grade code in Python/SQL, and contribute to semantic data layers enabling self-serve metrics for executives and teams.

Qualifications

• Translate ambiguous business problems into measurable questions.
• Explain technical concepts to non-technical stakeholders.
• Design and evaluate growth experiments (A/B tests).
• Write production-grade Python and SQL for large-scale data environments.
• Work with GTM metrics such as ARR, CRR, and churn.

Responsibilities

• Embed with business teams to become a trusted analytics partner.
• Perform exploratory data analysis to uncover trends and drivers.
• Build and productionize ML models (propensity, PQL, churn, expansion).
• Design, run, and evaluate growth experiments (A/B tests).
• Develop agentic workflows and automate analysis pipelines.
• Contribute to GitLab’s Semantic Layer for self-serve metrics.
• Write clean, scalable Python/SQL in a high-scale environment.

Skills

GTM analytics

SQL (Snowflake)

Python for ML

A/B testing

Communication to non-technical

Tools

Looker

Tableau

Hex

MLflow

Jupyter

LightGBM

XGBoost

Job description

GitLab is seeking a Data Scientist for the Enterprise Analytics team to forecast go-to-market performance and size opportunities. You will apply statistics, experimentation, and machine learning to drive activation, conversion, expansion, and retention across GTM metrics.

You’ll embed with Sales, Revenue Operations, and Finance, build production-grade code in Python/SQL, and contribute to semantic data layers enabling self-serve metrics for executives and teams.