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G2 is the world's largest software marketplace, recently merged with Capterra, SoftwareAdvice, and GetApp to create a unified platform serving 200M+ annual visitors with 6M verified reviews. The company is transforming the B2B software industry by becoming the most trusted data foundation for software buyers and sellers in the age of AI.
As Principal Data Scientist, you will be a senior individual contributor within G2's Product organization, reporting to Product Leadership. This is a hands-on technical role where you'll spend significant time building models, designing auctions, and shipping production systems, while also mentoring team members and evangelizing data science best practices across the company.
Key responsibilities include:
- Owning the experimentation standard for G2 and G2 Digital Markets by consolidating practices across properties into a single, documented, defensible approach covering experiment design, randomization, metric definition, guardrails, sample size, duration, and result interpretation.
- Influencing engineering and product teams to adopt experimentation standards through direct partnership on instrumentation, assignment, and platform behavior, making the correct approach the low-friction option via tooling, templates, and review processes.
- Defining end-to-end holistic experimentation requirements, including platform capabilities, telemetry, metric layers, analysis tooling, review processes, and organizational habits.
- Designing experimentation methods suited to G2's actual traffic and iteration constraints, building a portfolio of approaches for different volume regimes and decision speeds (high-throughput testing, variance reduction, sensitivity techniques, and sound alternatives to randomized tests).
- Driving exploration of modern experimentation methods, evaluating emerging approaches like Bayesian decision frameworks, always-valid inference, sequential testing, multi-armed bandits, and variance reduction methods, with judgment on when each is appropriate.
- Evangelizing data science impact across product, engineering, and business stakeholders, improving how experiment results are interpreted and communicated, and holding the organization accountable to what tests actually demonstrate.
Your ultimate goal is to play 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'll partner with stakeholders to modernize existing products and develop new ones for a rapidly evolving, AEO-driven (answer engine optimization), privacy-constrained landscape.
REQUIREMENTS:
- 8+ years of relevant experience in data science, applied statistics, or closely related field with substantial focus on experimentation; OR PhD in quantitative discipline plus 6+ years
- Deep expertise in experimental design and causal inference, including working knowledge of failure modes of online controlled experiments (power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)
- Working command of both frequentist and Bayesian approaches with judgment to choose between them for a given decision
- Demonstrated experience designing experimentation methodology for real products—not only analyzing tests but deciding how they should be run
- Track record of setting technical standards that other teams actually adopted and influencing engineering partners without direct authority
- Strong applied experience with time series and web/behavioral data at scale
- Fluency in Python and SQL, comfort with modern data ecosystems (Snowflake, Spark, Airflow, dbt)
- Exceptional written and verbal communication, including ability to explain methodological trade-offs to non-technical stakeholders and defend to skeptical technical ones
STAND-OUT QUALIFICATIONS:
- 10+ years of relevant experience including ownership of experimentation practice or platform at company running experiments across multiple products or properties
- Hands-on experience taking modern inference methods into production (always-valid p-values, sequential testing, bandit-based allocation)
- Experience with low-traffic or low-power experimentation problems and quasi-experimental methods where randomization is constrained (geo tests, synthetic control, difference-in-differences, switchback designs)
- Experience with interference, network effects, or two-sided marketplace experimentation
- Experience as technical lead setting direction for groups, mentoring senior data scientists, reviewing designs and analyses
- Experience with ML or LLM system evaluation (offline/online evaluation design, metric development)
- Contributions to experimentation community through publications, open-source work, or conference participation
- PhD in quantitative discipline or MS in STEM
