Sr. Data Scientist

The Senior Data Scientist is a highly skilled individual contributor responsible for leading complex analytical and modeling efforts using CAQH’s healthcare datasets. This role builds on the core Data Scientist position by taking greater ownership of analytical design, modeling decisions, and delivery of insights for high-impact initiatives. 

The Senior Data Scientist independently leads projects of moderate-to-high complexity, develops and validates advanced models, and works closely with engineering and business partners to translate analytical approaches into production-ready solutions. This role operates with limited oversight and is expected to apply strong technical judgment when working in ambiguous problem spaces. 

The Senior Data Scientist is a full-time, remote, exempt position and reports to the Sr. Director, Data Science & Analytics. 

Base Salary Range: $120,000 - $140,000 annually. 

Specific Responsibilities: 

Lead the development, validation, and refinement of advanced statistical and machine learning models for complex business problems. 

Serve as the primary analytical owner for assigned initiatives, with accountability for model quality, analytical rigor, and timely delivery of results. 

Design analytical approaches and modeling strategies, translating business questions into well-defined technical solutions. 

Perform advanced feature engineering, exploratory data analysis, and model evaluation using large, complex healthcare datasets. 

Partner with Data Engineering and Information Systems teams to translate modeling approaches into production-ready solutions, while engineering teams own deployment and operations. 

Support production models through performance analysis, monitoring, and retraining activities. 

Design and execute experiments to test hypotheses and measure the impact of analytical solutions. 

Evaluate new data sources and assess their suitability, quality, and limitations for modeling and analysis. 

Communicate analytical findings, model behavior, and key assumptions to stakeholders with varying levels of technical expertise. 

Document analytical methods, decisions, and results to support reproducibility and knowledge sharing. 

Provide peer-level technical guidance and code review to Data Scientists and Analysts, supporting their development without formal leadership responsibility. 

Contribute reusable code, features, and analytical assets to shared repositories and team standards. 

Supervisory Responsibilities: 

None. 

Skills:  

Advanced proficiency in Python, R, and SQL for statistical analysis, modeling, and feature engineering. 

Strong hands-on experience with statistical and machine learning techniques, including regression/GLM, tree-based methods, boosting, clustering, and basic text analytics. 

Proven experience developing, validating, and tuning models for real-world use cases. 

Experience supporting models in production environments, including collaboration with engineers on deployment and monitoring. 

Solid understanding of model evaluation, experimental design, and performance metrics. 

Strong data wrangling skills and experience working with large, complex datasets. 

Ability to create clear, compelling data visualizations and analytical narratives using tools such as Power BI, Tableau, or R/Shiny. 

Ability to translate business problems into analytical approaches with limited guidance. 

Strong written and verbal communication skills for working effectively with cross-functional stakeholders. 

Experience following best practices for reproducible research, version control, and documentation. 

Ability to provide constructive peer feedback and informal mentorship. 

Demonstrated curiosity and willingness to learn new tools, methods, and domains. 

Experience:

4–7 years of experience building statistical or machine learning models using large datasets

Education: 

Advanced degree in Computer Science, Engineering or relevant field; PhD in Data Science or another quantitative field is preferred.