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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates deep expertise in experimental design and causal inference, with a strong command of both frequentist and Bayesian approaches. Proven ability to mentor team members and influence stakeholders while developing and operationalizing modern experimentation methods.
Highest-signal resume keywords
Experimental Design ExpertiseCausal Inference KnowledgeFluency in PythonExperience with SnowflakeTechnical Leadership
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Applied StatisticsExperimentation MethodologyTime Series AnalysisWeb/Behavioral Data AnalysisPower and Sensitivity AnalysisVariance ReductionMultiple Testing CorrectionHeterogeneous Treatment EffectsModern Inference MethodsLow-Traffic Experimentation
Soft Skills
Exceptional Written CommunicationExceptional Verbal CommunicationMentoring
Tools & Technologies
SQLSparkAirflowDbt
Certifications & Qualifications
PhD in Quantitative DisciplineMS in STEM
Industry Keywords
Data ScienceExperimentation StandardsAEO-Driven LandscapePrivacy-Constrained EnvironmentTechnical Standards
Tech Stack
Tools & technologiesAirflowPythonSparkSQL
About the role
Key responsibilities & impact- Shape how experimentation is conducted and how data science informs product decisions
- Connect software vendors with the right buyers through G2 products, including ads, reviews, and agentic evaluation
- Partner with stakeholders to modernize existing products and develop new products in an AEO-driven, privacy-constrained landscape
- Build models, design auctions, and ship production systems as a hands-on individual contributor
- Mentor team members and evangelize data science best practices across the company
- Own the experimentation standard for G2 and G2 Digital Markets
- Consolidate practices into a documented approach covering experiment design, randomization, metrics, guardrails, sample size, duration, and interpretation
- Work with engineering teams on instrumentation, assignment, and platform behavior
- Define end-to-end experimentation capability requirements, including platform, telemetry, metric layers, analysis tooling, review process, and organizational practices
- Develop methods for different traffic volumes and decision speeds
- Evaluate and operationalize modern experimentation methods
- Teach data science across product, engineering, and business stakeholders
- Improve the quality of experiment-result interpretation and communication
Requirements
What you’ll need- 8+ years of relevant experience in data science, applied statistics, or a closely related field, with a substantial portion focused on experimentation; or a PhD in a quantitative discipline plus 6+ years
- Deep expertise in experimental design and causal inference
- Working knowledge of failure modes of online controlled experiments, including power and sensitivity analysis, variance reduction, multiple testing correction, and heterogeneous treatment effects
- Working command of both frequentist and Bayesian approaches
- Demonstrated experience designing experimentation methodology for a real product
- Track record of setting technical standards adopted by other teams
- Experience influencing engineering partners without authority
- Strong applied experience with time series and web/behavioral data at scale
- Fluency in Python and SQL
- Comfort with modern data ecosystems such as Snowflake, Spark, Airflow, and dbt
- Exceptional written and verbal communication
- United States work authorization without restriction; sponsorship question included in application
- Preferred: 10+ years of relevant experience
- Preferred: hands-on experience with modern inference methods in production
- Preferred: experience with low-traffic experimentation and quasi-experimental methods
- Preferred: experience with interference, network effects, or two-sided marketplace experimentation
- Preferred: technical leadership, mentoring, and design/analysis review experience
- Preferred: ML or LLM evaluation experience
- Preferred: publications, open-source work, or conference participation
- Preferred: PhD in a quantitative discipline or MS in STEM
Benefits
Comp & perks- Flexible work
- Ample parental leave
- Unlimited PTO
- Equitable and inclusive workplace
- Professional growth supported by a company-wide career framework
- Human oversight in hiring decisions
- Option to opt out of AI-assisted application review without disadvantage