Principal Data Scientist

🕒 3 days ago

🇺🇸 United States – Remote

💵 $190k - $235k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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G2

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

G2 is a business software review platform that provides insights to potential buyers through over 2. 5 million real user reviews. The platform covers a wide range of software categories including project management, video conferencing, e-commerce platforms, CRM, AI chatbots, and many more. G2 empowers businesses to discover and compare software options to make informed purchasing decisions. It serves as a resource for marketers, sales professionals, IT and finance departments to optimize their software investments. Additionally, G2 offers solutions for software vendors to enhance their profiles and reach more buyers.

📋 Description

• 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

• 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

• 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