Data Scientist, Enterprise Analytics
New
G
GitLabDevSecOps software
Location: Remote, United StatesFull-TimeMiddle
Salary86,400 - 176,400 USD per year
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Job Details
- Experience
- 3-5 years of professional experience in GTM analytics, data science, or a similar technical role
- Required Skills
- PythonSQLNumpyPandasscikit-learn
Requirements
- Have 3-5 years of professional experience in GTM analytics, data science, or a similar technical role.
- Translate ambiguous business problems into clear, measurable questions and communicate technical concepts to non-technical stakeholders.
- Have experience with experimentation and causal inference, including A/B testing, power analysis, and quasi-experimental methods.
- Know statistical and machine learning methods such as regression, classification, clustering, and survival analysis.
- Have hands-on SQL experience in cloud data warehouses such as Snowflake, including product usage and event data.
- Have experience with business intelligence tools such as Looker, Tableau, or Hex.
- Use Python for statistical modeling, machine learning, and production data science, with hands-on experience in pandas, NumPy, scikit-learn, and Jupyter.
- Have experience with libraries such as LightGBM and XGBoost.
- Have hands-on experience building agentic workflows with AI coding agents such as Claude Code or OpenCode, including evaluating output for accuracy and reliability.
- Have experience working in a B2C company with metrics including Consumption, CRR, ARR, and Churn & Contraction.
- Be able to use GitLab and work effectively in a fully remote organization.
Responsibilities
- Partner with Sales, Revenue Operations, and Finance to generate insights and support strategic decision making across GTM.
- Perform exploratory data analysis to identify behavioral trends, long-term patterns, and drivers of business performance.
- Build and productionize machine learning models, including propensity, PQL scoring, churn, and expansion models.
- Apply MLOps practices and tooling, including CI/CD for ML pipelines, model registries, automated retraining, and monitoring.
- Design and evaluate A/B tests and quasi-experiments for onboarding, pricing and packaging, and upgrade flows.
- Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring.
- Contribute to the Semantic Layer so stakeholders and agents can self-serve metrics using trusted and tested sources.
- Write and review production-grade Python and SQL for maintainability and performance in a high-scale database environment.
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