Data Scientist 6 - Experimentation Platform

Accountabilities

• • Define and influence the strategic direction of the experimentation platform, including user experience, workflows, metrics, reporting, templates, and other capabilities that enable data scientists to run high-quality experiments efficiently.
  • Establish and continuously improve standards for experimentation and causal inference, covering areas such as peeking, covariate adjustment, any-time-valid methods, metric definitions, allocation mechanisms, and analysis practices.
  • Ensure that experiment allocation, logging, data processing, and inference methods are trustworthy, statistically sound, and capable of being verified through automated, recurring, and monitored processes.
  • Serve as a strategic partner to data science and engineering teams, translating data science needs into scalable platform capabilities and representing those needs with engineering leadership.
  • Drive the consolidation of fragmented and bespoke experimentation systems into a coherent, modern platform that makes recommended practices the easiest path for teams to follow.
  • Translate ambiguous experimentation challenges into a prioritized product roadmap, determining which capabilities should be built into the platform, exposed as self-service tools, or deliberately excluded.
  • Influence experimentation practices across teams with different levels of maturity and across evolving business areas, ensuring consistent standards and methodological rigor.
  • Mentor colleagues working with or on the platform and represent the platform's perspective in organization-wide discussions about experimentation methodology.

  Requirements

  • Advanced degree such as a PhD or Master's in Computer Science, Statistics, Economics, Applied Mathematics, or another quantitative discipline.
  • 8+ years of experience applying statistics and causal inference to experimentation, including designing experiments at scale and diagnosing methodological or operational failures.
  • Proven experience establishing standards or developing tools that have been adopted across multiple teams or an entire organization, rather than only providing project-specific analytical guidance.
  • Deep practical understanding of experimentation risks, including sample ratio mismatches, winner's curse, regression to the mean, false discovery rates across test portfolios, peeking, covariate adjustment, and allocation-versus-analysis-unit mismatches.
  • Experience converting complex or ambiguous data science problems into a clear, sequenced product roadmap and making sound decisions about platform capabilities and self-service functionality.
  • 5+ years of experience working with data science programming languages, ideally including Python and SQL, with the ability to collaborate closely with engineers on APIs, schemas, and system architecture.
  • Exceptional communication and stakeholder-management skills, with the ability to influence both highly technical audiences and non-technical business partners, including stakeholders who may initially be skeptical of new methods or standards.
  • Strong curiosity and intellectual flexibility, with an interest in learning new statistical methods and optimization techniques while demonstrating the judgment to favor established approaches when they are more appropriate.
  • Strong product sense, strategic thinking, and the ability to operate effectively in an environment where experimentation is central to continuous improvement.

  Benefits

  • Annual compensation range of $491,000–$775,000, varying according to location, job family, background, skills, experience, and relevant market indicators.
  • Compensation is structured around annual salary and stock options, with employees able to choose each year how much of their compensation to allocate between the two.
  • Comprehensive health insurance plans and mental health support.
  • 401(k) retirement plan with employer matching.
  • Stock option program.
  • Disability programs, Health Savings Accounts (HSA), and Flexible Spending Accounts (FSA).
  • Family-forming benefits and life and serious-injury benefits.
  • Paid leave of absence programs.
  • Flexible time off for full-time salaried employees.
  • A remote, full-time working arrangement within the United States.
  • An inclusive environment committed to meaningful interview experiences and reasonable accommodations throughout the hiring process.