Data Scientist 6 - Experimentation Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist 6 - Experimentation Platform based in United States.

This is a high-impact Staff Data Scientist role focused on shaping experimentation at enterprise scale.
You will help define the strategy, standards, tooling, and product direction for a company-wide experimentation platform.
The role sits at the intersection of data science, causal inference, product thinking, and platform engineering.
You will elevate the rigor and trustworthiness of experimentation methods while making best practices easier to adopt.
You will partner closely with data scientists, engineers, and business stakeholders across diverse teams and domains.
A major focus will be consolidating fragmented experimentation practices into a unified, scalable, and maintainable platform.
This is an opportunity to influence how an entire organization makes data-driven decisions through experimentation.

• 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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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