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Experimentation Analyst Jobs in Ohio (NOW HIRING)

Experimentation Jobs

$80 - $100/hr

You will run and measure SEO experiments using A/B tests and Bayesian frameworks to quantify the impact on organic traffic. Additionally, you will deliver self-serve analytics through Power BI ...

Experimentation Jobs

$125 - $150/hr

As Head of Marketing Experimentation & Growth at onpier, you will build and lead a lean multidisciplinary Exploration Unit while establishing a structured model for hypotheses, tests, and validation.

Peraton

Data/Operations Research Analyst

Columbus, OH · On-site

$100 - $125/hr

Experience with operations research and statistical modeling techniques beyond the basics -- regression, time-series forecasting, clustering/segmentation, A/B or quasi-experimental analysis, linear ...

Posted today

Arenatechnologies

$80 - $100/hr

Experience with rapid acquisition or experimentation pathways (OTA, CSO, AFWERX‑like models). #J-18808-Ljbffr

Sierra Lobo, Inc.

Thermal Fluids Analyst

Milan, OH · On-site

Conduct experimentation and optimization on laboratory scale prototypes of advanced cryogenic technologies. * Conduct design of experiments and experimental error analyses. * Support large and small ...

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Experimentation Analyst information

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.

What are popular job titles related to Experimentation Analyst jobs in Ohio?

For Experimentation Analyst jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Experimentation Analyst jobs in Ohio look for?

The top searched job categories for Experimentation Analyst jobs in Ohio are:

What cities in Ohio are hiring for Experimentation Analyst jobs?

Cities in Ohio with the most Experimentation Analyst job openings:

Experimentation Analyst jobs near you

Quantitative Analyst

Wright-Patt Credit Union Inc.

Beavercreek, OH • On-site

$100 - $125/hr

Other

Re-posted 16 days ago

Wright-Patt Credit Union rating

5.8

Company rating: 5.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

The Quantitative Analyst is responsible for leading high-impact statistical analysis, measurement design, and scalable analytics solutions that improve business performance and decision-making. This role partners closely with Strategy, Product, and Technology teams to evaluate key initiatives, identify performance drivers, develop statistically sound measurement approaches, and deliver executive-ready insights that influence priorities and investments. The Quantitative Analyst combines strong analytical depth with automation and repeatability, ensuring insights are accurate, timely, and operationally useful.

1) High-Impact Quantitative Analysis & Decision Science (30%)
  • Perform exploratory data analysis, segmentation, and trend analysis to uncover patterns and anomalies.
  • Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.
  • Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.
  • Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.
2) Experimentation, Testing, and Impact Evaluation (25%)
  • Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.
  • Partner with product and business teams to define success metrics, baselines, and measurement plans.
  • Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.
  • Develop standardized experiment readouts and decision frameworks to improve speed and consistency.
3) Predictive Analytics & Optimization (20%)
  • Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.
  • Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.
  • Support model evaluation using practical performance measures (lift, precision/recall, error rates).
  • Translate model outputs into actionable recommendations and operational workflows.
4) Automation & Scalable Analytics Delivery (15%)
  • Develop automated analysis workflows using SQL and Python to reduce manual effort.
  • Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.
  • Partner with data engineering teams to improve data availability and support repeatable pipelines.
  • Implement monitoring and alerting for key performance indicators and threshold-based changes.
5) Communication, Visualization, and Executive Enablement (10%)
  • Build clear, executive-ready summaries and visualizations tied to business outcomes.
  • Present findings and recommendations to senior leaders and cross-functional teams.
  • Communicate confidence levels, limitations, and tradeoffs in a practical way.
  • Ensures proper policies, procedures, risk mitigation activities, and operating controls are followed. Reports gaps in policies, procedures, and operating controls to leadership to ensure member impact and risk is mitigated.
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