Data Scientist II

Company: Sundayy

Location: Location not specified (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-05-29

About this role

About The Company
Tempus AI is a leading healthcare technology company dedicated to transforming clinical care through innovative data-driven solutions. Our proprietary platform integrates a comprehensive ecosystem of real-world evidence, including clinical, molecular, and claims data, to generate actionable insights for healthcare providers and pharmaceutical partners. By leveraging advanced AI and machine learning technologies, Tempus aims to enable precision medicine and improve patient outcomes across a variety of therapeutic areas, with a strong focus on oncology and personalized treatment strategies.


About The Role
The Data Scientist II, Real World Evidence (RWE) role at Tempus AI is a critical position within our Pharma R&D team. This role involves collaborating with major pharmaceutical partners to design, execute, and interpret observational studies using real-world clinical data. The ideal candidate will have a strong background in oncology and epidemiology, with expertise in study design, statistical analysis, and advanced data modeling. You will lead efforts to derive meaningful insights from complex real-world datasets, implement cutting-edge statistical and AI methodologies, and contribute to the development of reusable tools and best practices. Your work will directly support the advancement of clinical research and drug development, helping to identify optimal treatment pathways and improve patient care.


Qualifications

  • PhD in epidemiology, biostatistics, data science, public health, or a related field
  • Or Master’s degree with 2+ years of relevant work experience
  • Proficiency with observational healthcare data analysis, including survival analysis and time-to-event methodologies
  • Strong experience in executing real-world data analytical studies
  • Proficiency in R and SQL, with familiarity in statistical packages and tools
  • Experience applying machine learning techniques and LLM-based coding assistants (e.g., Copilot, Cur...

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