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# data scientist

- **Company:** THE GREAT LIBRARY, LLC
- **Location:** Mountain View, CA, United States
- **Contract:** Permanent contract
- **Skills:** Data Cleansing, Data Governance, Machine Learning, Model Validation, Data Analytics
- **Published:** September 27, 2026
- **Apply:** https://www.juju.com/job/16_8cb325b3

## About the Role

* Strong experience applying statistical and machine learning methods to real-world problems.
 * Proficiency with data preparation and validation, and the ability to work confidently with imperfect or incomplete datasets.
 * Demonstrated experience designing analyses or experiments and interpreting results to support decisions.
 * Solid understanding of model evaluation, validation, and generalization to prevent common pitfalls.
 * Strong communication skills, including the ability to explain technical concepts to non-technical partners.

Preferred Qualifications

 * Experience building end-to-end analytical pipelines and taking models from development through production use.
 * Familiarity with responsible data practices, including privacy-aware handling and governance principles.
 * Experience mentoring teammates or contributing to technical standards for reproducibility and documentation.
 * Track record of delivering measurable impact through data science initiatives.

## Description

The Great Library Co. is seeking a data scientist to turn complex data into practical insights and models that help the business make better decisions. In this role, you will partner with cross-functional teams to design experiments, develop predictive and analytical solutions, and communicate results in a clear, action-oriented way.

You will be responsible for the full lifecycle of analytics and modeling-from framing problems and preparing data to building, validating, and deploying solutions-while maintaining high standards for accuracy, reproducibility, and thoughtful data governance.
Key Responsibilities

 * Build and refine data-driven models and analyses to solve business problems and improve performance metrics.
 * Collect, clean, and transform data from multiple sources to ensure analysis quality and reliability.
 * Design and evaluate experiments, including defining success metrics and interpreting results with statistical rigor.
 * Collaborate with engineering, product, and stakeholders to translate analytical findings into recommendations.
 * Present insights clearly through written documentation and stakeholder-friendly explanations, balancing detail with usability.


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