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# Data Scientist 3 (Product Analytics)

- **Company:** REMOTE HAND
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $153,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Python (Programming Language), Standard Sql, Usage Analysis, Data Processing, Tools for Reporting, Looker Analytics
- **Published:** August 19, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pdl3p85u3s

## About the Role

* Minimum of 5 years of experience in product analytics or data science.

 * Experience in healthcare, fintech, or complex e-commerce environments preferred.

 * Expert-level SQL skills and strong Python proficiency for data manipulation and analysis.

 * In-depth knowledge of statistical methods, experimentation, and A/B testing.

 * Proven ability to collaborate effectively with Product, Design, and Engineering teams.

 * Proactive, user-focused mindset with a strong commitment to improving customer experiences.

 * Familiarity with modern data tools such as dbt, Looker, or Hex preferred.

 * Authorization to work full-time in the United States.

## Description

The Data Scientist III (Product Analytics) drives data-informed decision-making across product development. This role analyzes user behavior and product performance to uncover opportunities for optimization, improve patient experiences, and measure the effectiveness of products and AI-powered personalization features. The position partners closely with Product, Design, and Engineering teams to translate complex data into actionable insights that influence product strategy and business outcomes.

Responsibilities

 * Collaborate with Product, Design, and Engineering teams to analyze user behavior and identify actionable insights.

 * Design, execute, and evaluate product experiments to improve patient experiences and business outcomes.

 * Conduct research to identify opportunities for product optimization.

 * Define, monitor, and analyze success metrics for product features and initiatives.

 * Develop scalable dashboards and reporting tools to distribute data insights across the organization.

 * Communicate complex statistical analyses and findings clearly to technical and non-technical stakeholders.


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