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Company Overview
A leading healthcare technology organization specializes in precision medicine and real-world evidence solutions. Their proprietary platform connects a full ecosystem of clinical and molecular data to generate actionable insights, enabling physicians and pharmaceutical partners to deliver the right treatments to the right patients at the right time. The company focuses on leveraging AI, advanced analytics, and epidemiological methods to advance research and patient outcomes.
About the Role
The Real World Evidence (RWE) team within the Pharma R&D group partners with pharmaceutical companies to provide robust data analysis, methodological guidance, and real-world insights. The Senior Data Scientist II will lead observational studies, interpret complex clinical data, apply advanced statistical methods, and implement AI tools to augment research insights, particularly in oncology and epidemiology.
Key Responsibilities
Partner with pharmaceutical collaborators to independently execute RWE research plans leveraging the company’s multimodal platform
Lead derivation of complex real-world endpoints, demonstrating expertise in clinical and molecular data and methodological nuances
Contribute to methodological best practices, reusable code, and internal packages across collaborations
Integrate LLMs, agentic workflows, and AI tools to accelerate coding, analysis, and insight generation
Interpret and communicate RWE analysis results clearly to both technical and non-technical stakeholders
Prepare internal reports, external deliverables, manuscripts, and conference materials as appropriate
Collaborate cross-functionally with product, oncology, clinical abstraction, and data science teams to enhance data quality and analytics capabilities
Minimum Qualifications
PhD in Epidemiology, Biostatistics, Data Science, Public Health, or related field OR Master’s degree with 2+ years of additional work experience
Proficiency with observational healthcare data, including survival analysis and RWD studies
Proficiency in R and SQL, including statistical packages and tools
Experience applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor), and agentic frameworks for scientific workflows
Adherence to software engineering best practices (version control, modular code, documentation)
Excellent communication and client-facing skills; able to present complex analyses to diverse stakeholders
Strong project management and collaboration abilities; able to work in a fast-paced, multi-disciplinary environment
Preferred Skillsets
Experience in Pharma or drug development, clinical trial design (Phase II-III)
Analytical experience with claims, EHR, or registry data
Practical experience configuring or adapting LLMs for scientific workflows
Knowledge of oncology guidelines (e.g., NCCN)
Experience with biomarker or molecular data (e.g., genomics)
Experience with cloud platforms such as AWS, BigQuery, or Google Cloud Platform (GCP)
Disclaimer
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