Junior Variant Scientist
Passionate about making a difference in the world of cancer genomics?
With the advent of genomic sequencing, we can finally decode and process our genetic makeup. We now have more data than ever before, but providers don't have the infrastructure or expertise to make sense of this data. We are on a mission to connect an entire ecosystem to redefine how genomic data is used in clinical settings. We are looking for a Junior Variant Scientist who will work with our Clinical and Computational Biology Team on reports for clinical and research use.
Duties and Responsibilities Include:
- Analyze somatic and inherited genetic test results to generate high-quality customized clinical reports
- Perform critical quality control functions in reporting workflow
- Assist in variant classification based on emerging scientific information
- Identify literature and analyze for accuracy and experimental robustness (i.e. experimental design, procedures, and results)
- Extract, structure, and analyze the latest scientific research to construct and maintain the Tempus cancer genomics knowledge database (KDB)
- Work with the clinical team and medical leadership to research and review therapeutic and prognostic evidence used in clinical reports
Required Experience and Skills:
- Minimum BS degree in Genetics, Molecular Genetics, Cancer Biology, or Biological Sciences
- Experience reading and critically evaluating scientific literature
- Exceptionally detail-oriented with strong critical-thinking skills
- Excellent communication skills with the ability to work both independently and in a group setting
- Excitement and drive to make a difference in a fast-paced energetic work environment!
Ideal Candidate will Possess:
- MS degree in Human Genetics, Molecular Genetics, or Biology with an in-depth understanding of cancer biology
- Programing or scripting experience strongly preferred
- Familiarity with human mutation databases, genome browsers, HGVS nomenclature, and oncology therapies
- Research experience and handling of large datasets