Strategic Data Scientist, Enterprise Analytics (Remote)

GitLab; United States; Remote; USD 86,000 - 176,000; Full time

Benefits offered by this job

Equity Compensation & Employee Stock

Growth and Development Fund

Parental Leave

Flexible Paid Time Off

Job summary

GitLab is seeking a Data Scientist on the Enterprise Analytics team to partner with Sales, Revenue Ops, and Finance. You will apply statistical, experimental, and machine learning methods to forecast outcomes, explain drivers of activation and retention, and size opportunities at scale.

You will build predictive models (propensity, PQL, churn) and run growth experiments, while supporting company-wide reporting and GTM performance views used by executives.

Qualifications

• 3–5 years in GTM analytics or similar technical role.
• Translate business problems into measurable questions.
• Experience with experimentation and causal inference.
• SQL in cloud data warehouses and BI tools.
• Python for statistical modeling and ML in production.
• Hands-on AI agent workflows and evaluation.

Responsibilities

• Partner with Sales, Revenue Operations, and Finance to define go-to-market metrics.
• Perform exploratory data analysis to uncover trends and drivers.
• Build and productionize ML models (propensity, PQL, churn) with MLOps.
• Design and evaluate growth experiments (A/B tests).
• Develop agentic workflows for analysis and automation.
• Contribute to GitLab’s Semantic Layer for self-serve metrics.
• Write production-grade Python and SQL in a high-scale DB environment.

Skills

GTM analytics

Data science

Experimentation

SQL (Snowflake)

Python (statistical modeling)

ML & MLops

Causal inference

Looker/Tableau/Hex

Pandas/NumPy/scikit-learn

AI agents workflows

Tools

Snowflake SQL

Looker

Tableau

Hex

MLflow

Job description

GitLab is seeking a Data Scientist on the Enterprise Analytics team to partner with Sales, Revenue Ops, and Finance. You will apply statistical, experimental, and machine learning methods to forecast outcomes, explain drivers of activation and retention, and size opportunities at scale.

You will build predictive models (propensity, PQL, churn) and run growth experiments, while supporting company-wide reporting and GTM performance views used by executives.