GitLab Posted 6d ago

Data Scientist, Enterprise Analytics

Location

Remote

Full remote

Contract

Permanent

Full-time

Salary

$86–176k

Gross per year

Experience

3+ years

Mid-level level

Job summary

Responsibilities

As a Data Scientist at GitLab on the Enterprise Analytics team, you’ll be a trusted analytical partner to Sales, Revenue Operations, and Finance, helping define how we measure go-to-market performance and where we invest to grow. This is a strategic and hands-on role.

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…
Build and productionize machine learning models - such as propensity, product-qualified lead (PQL) scoring, churn, and expansion models - including training, validation, testing…
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…
Build and operationalize agentic workflows for analysis, modeling, and pipeline automation, with evaluation and monitoring that keep agent-generated insights accurate, traceable…

What they're looking for

3-5 years of professional experience in GTM analytics, data science, or a similar technical role
Demonstrated ability to translate ambiguous business problems into clear, measurable questions and communicate technical concepts effectively to non-technical stakeholders.
Experience with experimentation and causal inference (A/B testing, power analysis, quasi-experimental methods) and with statistical and machine learning methods such as…
Deep, hands-on understanding of SQL in cloud data warehouses (like Snowflake SQL), including working with product usage and event data, and in business intelligence tools…
Experience using Python for statistical modeling, machine learning, and production data science. With hands-on experience in pandas, NumPy, scikit-learn, Jupyter, and related…