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Experimentation Analyst Jobs (NOW HIRING)

Experimentation Jobs

$120 - $160/hr

You will design warehouse-native pipelines running analysis in customer warehouses such as Snowflake, Databricks, and BigQuery. Additionally, you will lead adaptive experimentation including ...

Upstart

$188 - $260/hr

The team partners closely with Machine Learning, Analytics, product, and engineering teams to bring greater coherence to Upstart's experimentation capabilities, improve experimentation rigor, and ...

JANUS Research Group

NY · On-site

$85 - $90/hr

The analyst converts learning and experimentation requirements into executable orders, sourcing actions, event support plans, and decision-quality updates for FCC G-3/5/7 leadership. Primary Duties ...

Home Depot

Sr. Decision Analyst, Experimentation

Atlanta, GA · On-site

$81K - $103K/yr

Explore new technical landscape in the world of Experimentation and bring it to the fold for Home Depot Key Responsibilities: * 65% - Data Analytics - Analyzes large volumes of data and leverages ...

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How much do experimentation analyst jobs pay per year?

As of Sep 7, 2026, the average yearly pay for experimentation analyst in the United States is $84,342.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,500.00 and $83,000.00 per year, depending on experience, location, and employer.

What is an experimentation analyst?

An Experimentation Analyst is a professional responsible for designing, implementing, and analyzing experiments—often A/B tests or controlled trials—to inform business decisions and optimize outcomes. They work closely with data, using statistical methods to determine the impact of changes to products, services, or processes. Experimentation Analysts collaborate with cross-functional teams to form hypotheses, establish test plans, and interpret results to provide actionable insights. Their work helps organizations make data-driven decisions and continuously improve their offerings.

What are some common challenges faced by experimentation analysts when designing and interpreting A/B tests?

Experimentation Analysts often encounter challenges such as ensuring statistically significant results, controlling for external variables, and avoiding biased sampling when designing and analyzing A/B tests. Interpreting results can also be complex, especially when dealing with ambiguous outcomes or when multiple experiments run simultaneously. Collaborating closely with product managers, engineers, and data scientists is critical to ensure that test designs align with business goals and that findings are actionable. Staying up to date with best practices in experimental design and data analysis helps address these challenges effectively.

What are the key skills and qualifications needed to thrive as an experimentation analyst, and why are they important?

To thrive as an Experimentation Analyst, you need strong analytical skills, a solid understanding of statistics, and experience with A/B testing, typically supported by a degree in a quantitative field. Proficiency with tools like SQL, Python or R, and experimentation platforms such as Optimizely or Google Optimize is commonly required. Attention to detail, critical thinking, and effective communication are valuable soft skills that help translate data insights into actionable recommendations. These skills are crucial for designing robust experiments, interpreting results accurately, and driving data-informed decisions within organizations.

What is the difference between Experimentation Analyst vs Data Analyst?

AspectExperimentation AnalystData Analyst
Required credentialsBachelor's in statistics, data science, or related field; familiarity with A/B testing toolsBachelor's in statistics, mathematics, or related field; proficiency in data visualization and analysis software
Work environmentCollaborates with marketing, product teams, and data science teams on testing initiativesWorks across departments to analyze data, generate reports, and support decision-making
Employer and industry usageCommon in tech, e-commerce, and digital marketing companies focusing on user experience optimization

The Experimentation Analyst primarily focuses on designing and analyzing A/B tests to optimize products and user experiences, often working closely with product teams. Data Analysts have a broader scope, analyzing large datasets to generate insights across various business functions. While both roles require strong analytical skills and familiarity with data tools, Experimentation Analysts specialize in testing methodologies, whereas Data Analysts focus on comprehensive data analysis and reporting.

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Experimentation Analyst jobs near you
Infographic showing various Experimentation Analyst job openings in the United States as of August 2026, with employment types broken down into 89% Full Time, 6% Part Time, and 5% Contract. Highlights an 82% Physical, 7% Hybrid, and 11% Remote job distribution, with an average salary of $84,342 per year, or $40.5 per hour.

New York & San Francisco, USA Patreon Senior Data Scientist, Feed Relevance

Experimentation Jobs

San Francisco, CA • On-site

$120 - $150/hr

Other

Re-posted 2 days ago

Job description

Why Here?

Patreon serves as a leading media and community platform where over 300,000 creators connect with fans through paid memberships, community chats, live experiences, and direct sales. The company has generated more than $8 billion in revenue since its start and boasts 10 million paying fans monthly. The Relevance team manages feed ranking and recommendations on key surfaces including Home Feed, Membership Feed, Post Page Recommendations, and Niches. This cross‑functional group builds personalized content systems from the ground up to boost fan engagement and creator monetization.

What Will You Do?

As a Senior Data Scientist, Feed Relevance at Patreon, you will own the analytics foundation for Patreon’s feed by building metrics infrastructure that supports dashboards, experiment analysis, and ML training data. You will design and analyze ranking experiments across Home Feed, Membership Feed, and other surfaces while partnering with ML Engineers to assess relevance changes. Additionally, you will conduct exploratory analyses on feed health and engagement patterns, then develop comprehensive dashboards to guide prioritization and measure impact.

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