data scientist

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.

• 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.