• Translate business needs into data analysis and actionable insights to optimize product performance and user experience for both existing and new products. • Work closely with product managers, engineers, and other cross-functional stakeholders to identify critical product questions and provide data-driven solutions. • Develop and maintain a scalable, reliable, and accurate analytics infrastructure to support product decisions. • Establish and maintain best practices for data collection, analysis, and reporting. • Deliver actionable insights and recommendations focused on landing tangible product impact. • Help foster a culture of data-driven decision-making and continuous improvement within the product teams. • Ensure the integrity and accuracy of the data used for analysis and reporting. • While the primary focus is on existing products, support 0 to 1 product launches by identifying key metrics and analyzing early-stage product data when applicable.

Role Description The Senior Data Scientist is a highly skilled individual contributor responsible for leading advanced statistical and machine learning initiatives to solve complex business challenges using large-scale healthcare data. This role owns the end-to-end analytical process, including designing modeling approaches, developing and validating models, and translating business questions into technical solutions. The Senior Data Scientist partners closely with data engineering and cross-functional teams to operationalize models and ensure their performance and scalability in production environments. Success in this role requires strong technical judgment, the ability to communicate insights to diverse stakeholders, and a commitment to analytical rigor and reproducibility. The Senior Data Scientist is a full-time, remote, exempt position and reports to the Sr. Director, Data Science & Advanced Analytics. Qualifications - 4–7 years of experience building statistical or machine learning models using large datasets - Advanced degree in Computer Science, Engineering or relevant field; PhD in Data Science or another quantitative field is preferred. Requirements - 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. Benefits - Competitive compensation - Comprehensive benefits package for full-time employees, including medical, dental, and vision coverage - 401(k) with company contributions and matching - Paid parental leave - Tuition assistance - Generous paid time off - Commitment to investing in our people and supporting professional growth over time Company Description DataSpring is the trusted data connector at the core of healthcare. For more than 25 years, we have powered the industry with the largest and most complete healthcare data foundation in the U.S., including more than 4.8 million provider data records sourced directly from providers and member data representing 75% of covered lives supplied by health plans. By improving how essential information flows across the system, DataSpring helps healthcare operate more efficiently, accurately, and with greater confidence.

• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. • Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics. • Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed. • Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics. • Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.