• Advance signal detection and early alerting by leveraging new technologies to reduce false positives and strengthen trust in results through reproducible analysis approaches and controls. • Standardize signal review outputs (evidence summaries, trend context, case-series characterizations) and ensure consistent governance across diverse data sources. • Maintain end-to-end signal tracking and lifecycle documentation so conclusions, decisions, and actions remain transparent and traceable. • Continuously improve pharmacovigilance data quality (completeness, consistency, de-duplication, coding, timeliness) and deliver “easy-to-digest” views (dashboards, scorecards, automated reports) to increase transparency into safety trends and data reliability. • Enable leadership-ready insight delivery through a regular cadence of updates and ad hoc escalations, with summaries focused on what changed, why it matters, and what’s next; contribute to structured quarterly reviews for key products. • Partner with PV leadership and digital/automation teams to evolve pharmacovigilance technology and signal management capabilities - translating needs into clear requirements and acceptance criteria, supporting testing and adoption (training, feedback loops), and ensuring continuity with minimal disruption.

Role Description The Data & AI Summer Intern will gain hands-on experience supporting machine learning, statistical analysis, and data-driven application development. This internship is designed as a learning-focused opportunity where the intern will work alongside data scientists, engineers, and product partners to help build analytics pipelines, translate analytical work into user-facing tools, and explore how AI-assisted development tools are used in real-world environments. The ideal intern should be familiar with how to use agent AI tools and familiar with applying AI-assisted coding and analysis tools (e.g., Codex-style or agent-based coding assistants) to accelerate development, debugging, and model experimentation workflows. Throughout the summer, the intern will contribute to meaningful projects while developing practical skills in Python, data engineering, ML workflows, and visualization. Responsibilities - Support ML model development and statistical analysis projects, including dataset preparation, feature engineering, model training, evaluation, and interpretation. - Assist in refactoring and translating existing R-based analysis workflows into well-structured, tested Python code. - Help build and integrate analysis outputs into interactive applications for visualization and exploration (e.g., Streamlit, Dash). - Contribute to developing modular, reusable components for analytics pipelines (data processing, metrics, reporting, and visualization). - Learn and apply software engineering best practices, including Git-based workflows, code reviews, documentation, and maintainable project structure. - Support automated testing, formatting, and CI/CD workflows under guidance from experienced engineers. - Collaborate with stakeholders to understand requirements and translate analysis/model results into clear visualizations and usable application features. - Assist with debugging and performance improvements across data/ML and application layers. What You’ll Gain - Hands-on experience with real-world data, ML models, and production-grade analytics workflows. - Exposure to modern data and AI engineering tools, practices, and team collaboration. - Mentorship from experienced data scientists and engineers. - The opportunity to contribute to a summer project and showcase your work at the end of the internship. Qualifications - Bachelor’s degree in computer science, Data Science, Statistics, Engineering, or a related field; Master or PhD student (or soon-to-graduate) strongly preferred. - Proficiency in Python for data analysis and software development. Company Description CareDx, Inc. is a leading precision medicine solutions company focused on the discovery, development, and commercialization of clinically differentiated, high-value healthcare solutions for transplant patients and caregivers. CareDx offers products, testing services, and digital healthcare solutions along the pre- and post-transplant patient journey, and is the leading provider of genomics-based information for transplant patients.

• Act as the cross functional owner of all aspects of Data Products including product development, partnerships, operations, and go-to-market • Establish Tap as the leading source for secondary trading insights for institutional investors and secondaries funds • Partner with LPs, GPs, and data vendors to acquire relevant private fund data over time • Create and maintain models behind valuation estimates, indices, and other Data Products • Iterate with clients, product, design, and engineering to incorporate Data Products into the broader Tap platform • Generate periodic reports and topical research utilizing Data Products • Drive adoption and awareness of Tap by syndicating Data Products into news publications, portfolio management systems, service providers, and other third parties • Devise and structure new data-driven transaction modalities like instant transactions or synthetic trading • Contribute to the big picture of company strategy and vision

• Support risk adjustment and quality initiatives for Value-Based Care programs across Medicare Advantage, ACA health plans, and Medicaid • Build and maintain risk-adjustment analytic modeling (HCC suspecting, recapture models, RAF forecasting) • Apply NLP and GenAI to medical records and clinical notes to extract structured clinical evidence • Develop LLM-assisted and agentic workflows to support medical record coding review, evidence summarization, and improved productivity and accuracy • Perform forecasting and scenario analysis tied to operational capacity and financial impact • Monitor model performance, bias, drift, and documentation sufficiency • Ensure analytics are explainable, auditable, and compliant • Partner closely with Product, Engineering, Coding, Clinical, Compliance, and Operations teams • Translate analytics into actionable signals embedded in payer workflows