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On Call Data Scientist Experimentation Jobs (NOW HIRING)

MDAEdge

Data Scientist & Experimentation Analyst

MDAEdge is seeking a Data Scientist & Experimentation Analyst to support the development and evaluation of ML-driven pricing and personalization solutions. This role involves providing data-driven ...

FanDuel

Data Scientist, Experimentation

New York, NY · On-site

The Data Scientist will partner with retention marketing stakeholders and will be responsible for designing and analyzing experiments that help to answer key questions and drive promotional strategy.

FanDuel

Data Scientist, Experimentation

Manhattan, NY · On-site

The Data Scientist will partner with retention marketing stakeholders and will be responsible for designing and analyzing experiments that help to answer key questions and drive promotional strategy.

PlayStation Global

OR

Staff Data Scientist - Experimentation & Measurement Overview: As a Staff Data Scientist on the Decision Science team at PlayStation, you will take a leading role in designing and interpreting ...

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On Call Data Scientist Experimentation information

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$37.5K

$122.7K

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How much do on call data scientist experimentation jobs pay per year?

As of Aug 14, 2026, the average yearly pay for on call data scientist experimentation in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an On Call Data Scientist Experimentation?

To thrive as an On Call Data Scientist Experimentation, you need a strong background in statistics, experimental design, and data analysis, typically supported by a degree in a quantitative field. Proficiency with programming languages like Python or R, familiarity with A/B testing platforms, and experience using data visualization and analytics tools such as SQL and Tableau are essential. Strong problem-solving skills, clear communication, and the ability to quickly adapt to changing priorities set standout candidates apart. These skills are crucial for generating actionable insights from experiments and ensuring data-driven decision-making in dynamic environments.

What is the difference between On Call Data Scientist Experimentation vs Data Scientist?

AspectOn Call Data Scientist ExperimentationData Scientist
CredentialsTypically requires a master's or PhD in data science, statistics, or related fieldsSimilar educational background, often with additional certifications or experience
Work EnvironmentOften on-demand, supporting experimentation projects across teams, flexible hoursRegular office hours, working on ongoing data analysis and modeling tasks
Employer & Industry UsageUsed in tech, e-commerce, and consulting firms for rapid experimentation supportCommon across industries for data analysis, modeling, and strategic insights

In summary, On Call Data Scientist Experimentation focuses on providing immediate, project-based support for experimentation efforts, often with flexible hours. In contrast, Data Scientists typically work on continuous data analysis and modeling within a structured schedule. Both roles require similar credentials but differ mainly in scope and work environment.

What does an On Call Data Scientist Experimentation do?

An On Call Data Scientist Experimentation is responsible for designing, executing, and analyzing experiments—such as A/B tests—to help organizations make data-driven decisions. They are available on an as-needed basis, often to address urgent issues, analyze experiment results, or troubleshoot problems in ongoing tests. Their work ensures that business strategies are informed by reliable, statistically sound data, and they often collaborate with product, engineering, and analytics teams to optimize processes and outcomes.

How does an On Call Data Scientist Experimentation typically collaborate with product and engineering teams during the experiment lifecycle?

On Call Data Scientist Experimentation roles work closely with product managers and engineers throughout the experiment lifecycle. They help design experiments, ensure accurate implementation, and analyze results to guide data-driven decisions. Regular communication is essential, as data scientists must translate technical findings into actionable insights for cross-functional teams. This collaborative environment fosters rapid iteration and ensures experiments align with business goals.
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On Call Data Scientist Experimentation jobs near you
Infographic showing various On Call Data Scientist Experimentation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Cupertino, United States On-site Apple Senior Data Scientist, Experimentation & Causal Inference

Experimentation Jobs

Cupertino, CA • On-site

$120 - $150/hr

Other

Posted 9 days ago

Job description

Why Here?

Apple is seeking a Senior Data Scientist, Experimentation & Causal Inference to help advance the scientific foundations of measurement, experimentation, and organizational learning across Apple Services. The role sits at the intersection of statistics, causal inference, experimental design, and decision-making. This position plays a pivotal role in establishing experimentation standards, developing advanced causal methodologies, building experimentation intelligence systems, and driving cross-experiment learning initiatives.

What Will You Do?

As a Senior Data Scientist, Experimentation & Causal Inference at Apple, you will own key components of the experimentation science ecosystem and work across product, growth, engineering, data engineering, and strategic science teams to define measurement frameworks. You will establish experimentation standards, develop advanced causal methodologies, and build experimentation intelligence systems for cross-experiment learning initiatives. Additionally, you will help evolve experimentation methodologies to evaluate increasingly complex product behaviors and long-term user outcomes.

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