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# Experimentation Data Scientist

- **Company:** AllSTEM Connections
- **Location:** Ontario, CA, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** A/B Testing, Data Visualization, Statistical Hypothesis Testing, Python (Programming Language), Regression Analysis, Adobe Target, Pyspark, Information Technology, Statistics Packages, Optimizely
- **Published:** August 18, 2026
- **Apply:** https://www.dice.com/job-detail/cd03d690-f682-4cb3-adc0-541243ede561

## About the Role

Education: Bachelor's degree in a quantitative field such as Statistics, Computer Science, Mathematics, or a related discipline (Graduate degree preferred).
Experience Baseline: Minimum of 3+ years of professional experience in data science, with a primary focus on digital experimentation and product analytics.
Technical Mastery:
oHigh proficiency in SQL and statistical programming languages (Python and/or R). Familiarity with PySpark is a strong plus.
oProven hands-on experience supporting and designing A/B tests at scale to drive product development.
oSolid expertise in regression analysis techniques (linear, logistic, etc.) and handling non-normal data distributions.
oFamiliarity with enterprise A/B testing platforms (e.g., Optimizely, Adobe Target, or custom internal frameworks).
Core Competencies: Exceptional communication and presentation skills; comfort with ambiguity; proactive self-starter capable of thriving with minimal oversight in a fast-paced environment.

## Description

We are seeking an analytical, strategic, and results-driven Experimentation Data Scientist to join our growing experimentation team. In this role, you will play a pivotal part in driving data-informed decision-making that directly impacts product development, user experience, and enterprise growth. You will partner closely with product managers, designers, engineers, and platform teams to optimize the entire A/B testing lifecycle and scale experimentation practices.

This is a high-impact technical role combining rigorous statistical modeling with cross-functional product collaboration. You will design controlled experiments, collect and clean complex data sets, quantify causal impacts, and translate statistical findings into actionable business insights. If you excel at turning ambiguity into clarity and scaling automated insights, we want to hear from you., Experimental Design & Strategy
Hypothesis Testing: Collaborate with cross-functional product, engineering, and design teams to define clear hypotheses, select key performance metrics, and design controlled experiments (A/B tests, multivariate tests, etc.).
Lifecycle Optimization: Partner with experimentation platform teams to identify and implement continuous improvements throughout the testing lifecycle, ensuring smooth execution at scale.

Statistical Analysis & Modeling
Rigorous Evaluation: Collect, clean, and transform multi-source data to conduct rigorous statistical evaluations of experiment results and treatment effects.
Statistical Modeling: Apply advanced statistical models and regression techniques (linear, logistic, etc.) to quantify uncertainty, handle non-normal data distributions, and estimate causal impacts.
Data Visualization: Design compelling dashboards and visualizations to communicate experiment findings, trends, and strategic recommendations to both technical and executive stakeholders.

Collaboration & Continuous Improvement
Stakeholder Partnership: Act as a trusted advisor to business leaders, translating complex quantitative findings into clear product recommendations.
Tool Streamlining: Develop and streamline reliable tools and automated workflows to accelerate insight delivery across the organization.
Methodology Advancement: Stay current with emerging developments in experimental design, causal inference, and data science methodologies.


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