Senior Data Scientist (Experimentation) at G2

The Data Science team at G2 sits within the Product organization, supporting Product Leadership. As the principal data scientist, you will help shape experimentation standards and use data science to inform product decisions.

Your ultimate goal is to act as a matchmaker, connecting software vendors with the right buyers through G2’s product offerings, including ads, reviews, and agentic evaluation. As G2 expands into the agentic world, you will partner with stakeholders to modernize existing products and develop new ones for a rapidly evolving, AEO-driven (Answer Engine Optimization), privacy-constrained landscape.

This is a senior individual contributor role. You will spend your time as a hands-on contributor, building models, designing auctions, and shipping production systems. Additionally, you will mentor your team and evangelize data science best practices across the company.

Key Responsibilities:

• Own the experimentation standard for G2 and G2 Digital Markets, consolidating practices into a single, documented, defensible approach covering experiment design, randomization, metric definition, guardrails, sample size and duration, and how results get read and acted on.
• Influence engineering and product teams to adopt these standards. Work directly with engineering teams on instrumentation, assignment, and platform behavior, ensuring the correct approach becomes the low-friction option through tooling, templates, reviews, and consistent partnerships.
• Define the requirements for holistic experimentation at G2, determining platform requirements, telemetry, metric layers, analysis tooling, review processes, and organizational habits.
• Design methods that fit G2’s actual traffic and iteration constraints, building a portfolio of approaches suited to different volume regimes and decision speeds.
• Drive the exploration of modern experimentation methods, evaluating emerging approaches and operationalizing those that prove effective, with a considered perspective on their applicability.
• Evangelize data science and make its impact transparent. Teach the practice across product, engineering, and business stakeholders, raise the quality of experiment result interpretation and communication, and hold the organization accountable for the outcomes of tests.