Senior Data Scientist II, Real World Evidence (RWE), Pharma R&D

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

The Real World Evidence (RWE) group within the Pharma R&D team at Tempus works with major pharmaceutical partners to provide best-in-class data, analysis, and methodological guidance for Tempus’s real-world data offering.

We are seeking a highly motivated and solutions-oriented RWE Data Scientist II with experience and interest in oncology and epidemiological study design to join our team. This role requires the ability to lead observational studies, derive insights from complex real-world clinical data, implement advanced statistical methods, and leverage cutting-edge AI tools to scale tasks and augment insights. 

Responsibilities:

• Strategic Pharma Partnership: Lead the design and execute delivery of RWE analyses for key pharma clients. You will be responsible for translating complex drug development questions and into actionable research plans that use Tempus data for trial design and outcomes research.

• Real World Data Expertise & Technical Oversight: Lead the derivation of complex real-world endpoints using extensive coding, demonstrating deep comprehension of Tempus clinical and molecular data structures and complexity, while also serving as an expert on the methodological nuances and limitations of real-world data. 

• Methodological Standards & Mentorship: Set the technical standard for the team by implementing advanced methods in survival analysis, machine learning and predictive modeling. You will actively mentor more junior scientists, guiding their technical development, reviewing code, and developing tools that set best practices across the organization.

• AI-Enhanced Workflows: Drive the practical adoption of LLMs and agentic tools into your own and the broader team’s daily workflow. Your focus will be on using these technologies to improve the speed and accuracy of code development, documentation, and review.

• Scientific Leadership & Influence: Own the communication of high-stakes results to both internal executives and external partners. You will be responsible for the scientific integrity of all deliverables, including manuscripts, conference abstracts, and technical reports where appropriate. 

• Cross-Functional Collaboration: Collaborate with internal product, oncology, and clinical abstraction, and real-world data science teams to continually enhance Tempus data quality, products, and analytical best practice. You will proactively identify gaps in current products and ensure that customer feedback is represented in development of new products. 

• Oncology & RWE Domain Expertise: Maintain deep expertise in oncology clinical guidelines (e.g., NCCN) and emerging RWE methodologies. You will be responsible for translating these external shifts into internal strategy, ensuring that our research designs and data modeling stay ahead of the evolving oncology landscape and reflect the most current standard of care.

Minimum Qualifications:

• Education: Advanced education in epidemiology, biostatistics, data science, public health, or related fields, to the level of either:

  • PhD and 4+ years of additional work experience

  • Master’s degree and 6+ years of additional work experience

• Technical & Statistical Mastery: 

  • Expert-level proficiency in observational real-world healthcare data, specifically in designing and implementing complex time-to-event methodologies (survival analysis).

  • Track record of leading RWD analytical studies from initial scoping through to publication or dissemination.

  • Proficient in using R and SQL, especially statistical tools and packages.

  • Proficiency applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor) and agentic frameworks to support data analysis, code review, or scienti