Passionate about precision medicine and advancing the healthcare industry?
With the advent of genomic sequencing, digital imaging, and techniques for large-scale clinical data processing, we have powerful new weapons in the data-driven fight against cancer. We’re on a mission to connect an entire ecosystem to redefine how genomic and clinical data is used for evidence-based medicine.
We are looking for a highly analytical, self-motivated, and solution-oriented Biostatistician II with experience and interest in oncology and/or molecular data to join our Tempus Discover team supporting Medical Affairs. We work with Key Opinion Leaders in academia and the research community to generate novel insights with real-world data. We work directly with the Tempus Medical Sciences Team and oncology research leaders to understand their goals and translate their scientific questions into insight generation. Top candidates will have experience working with clinical and research data pipelines, and/or doing biostatistical, epidemiological, or computational biology research on observational healthcare data.
Responsibilities:
- Lead independent analysis of Tempus data in support of research initiatives in Medical Affairs, involving extensive coding and comprehension of Tempus molecular and clinical data
- Present insights from research studies to external research partners through formal presentations and communicate research findings effectively.
- Stay updated on methodological advancements in real-world studies, next-generation sequencing (DNA/RNA), and oncology guidelines.
- Ensure compliance with all relevant regulations and company procedures.
- Participate in publications and podium activities.
Required Experience:
- 1+ years of post-grad experience in computational biology, epidemiology, biostatistics, data science or related fields.
- 1+ post-grad experience with healthcare data.
- Computational skills using R and SQL, especially relevant statistical tools and packages.
- Experience deriving oncology-specific clinical insights using genomic data and deriving real-world endpoints using time-to-event methodologies within a retrospective database.
- Excellent project management skills, with proven ability to collaborate with teams of multi-disciplinary scientists to define and execute analysis plans to address scientific questions using omics datasets (genomics, transcriptomics).
- Thrive in a fast-paced environment and demonstrate ability to communicate technical concepts to non-technical stakeholders
- A collaborative mindset, coupled with a genuine eagerness to learn and a steadfast dedication to maintaining integrity in all endeavors.
Nice to have:
- Familiarity with cancer genetics, immunology, or molecular biology.
- Experience with engineering practices for research computing (Docker, Git, and Github, code review, Linux, cloud computing)
- Experience with AWS and/or Bigquery and/or Google Cloud Platform (GCP)
- Proficiency in machine learning techniques, with a nuanced understanding of the strengths and limitations of various approaches, particularly regarding predictive and prognostic algorithms in medical research.
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