Experimentation Data Scientist

About GrowthBook

At GrowthBook, we are building an open source feature flagging and A/B testing platform. If you love the data challenges faced by large tech companies, but want the ownership and flexibility of a small startup, then GrowthBook is for you.

We are a small team, distributed across the US, and backed by YCombinator and Khosla Ventures. We're helping companies release code quickly and confidently while measuring the impact of what they launch.

As an open source company, we are focused on bottom-up adoption and building a product that engineers love to use. We have an amazing open source community on Slack that gives us constant feedback, feature requests, and ideas. GrowthBook is already used in production by hundreds of companies and we're just getting started!

Some of the problems you'll be working on

• Building essential analytics tools for online experimentation
  • A power calculator that uses historical experiment and metric data to provide users with a clear and accurate understanding of needed experiment run times in their specific context.
  • Meta-analysis tools to measure the topline impact of individual experiments and groups of experiments. These tools will further experimenters understanding of what is working and what isn't across their entire experimentation program.
  • Decision and Stopping Rules. Users in GrowthBook must determine themselves when to stop an experiment and which variation to declare a winner. These decisions are hard, especially for non-technical users, and there's a big opportunity to provide smart recommendations backed by data.
• Develop the statistics engine. Design and implement advanced experimentation approaches such as multi-armed bandits, cluster randomization, using prior data intelligently in a Bayesian experimentation platform, and more.
• Building robust analysis pipelines. Improve the reliability and efficiency of analysis pipelines that work for a wide variety of companies. GrowthBook users store their data in over 10 different warehouses, operate at scales ranging from brand new startup to global corporations. General pipeline principles and specific SQL knowledge can be combined to serve this wide range of customers.

Requirements

• Expert in experimentation and/or causal inference
• Expertise in SQL, beyond ad-hoc queries, and Python
• Background in statistics or statistics-heavy fields; familiarity with both frequentist and bayesian statistics
• Fluent English and good communication skills
• Startup experience or interest working in a small company
• Bonus: Familiarity with JavaScript
• Bonus: Built or contributed to an A/B experimentation platform
• Bonus: Worked implementing and/or analyzing experiments
• Bonus: PhD in statistics or another data-heavy field

Benefits

• Generous compensation and employee-friendly equity in a fast-growing early-stage well-funded startup
• Unlimited time off
• Medical insurance including dental and vision (US)
• Generous parental leave
• Work-from-home stipend and IT budget
• Remote-first company with regular off sites in exciting locations (once Covid allows us to safely travel again)