What are the responsibilities and job description for the Computational Biologist position at Precision Life Sciences?
Computational Biologist
Seeking a highly motivated and experienced Senior Computational Biologist with expertise in biology and computational biology. The successful candidate will be responsible for analyzing early-stage biomarker and multi-omics data, mining publicly available data sources, and designing experiments to validate model predictions. This role requires communicating computational results and outcomes to a multidisciplinary team of scientists, external collaborators, and partners.
Job Responsibilities
- Lead analysis and interpretation of molecular and functional datasets from ongoing clinical evidence and preclinical studies.
- Leverage understanding of clinical disease/biology to drive and test hypotheses. Collaborate very closely with translational, clinical, and preclinical immunology teams.
- Design, implement, and document algorithms and pipelines, ensuring their alignment with the objectives of ongoing and future projects.
- Formulate research hypotheses and develop computational methods to enhance biological interpretation of predictive models.
- Build initial prototypes of predictive models in the space of cancer and immunological biology, drug response, and patient outcome prediction.
- Clearly communicate analysis results including key takeaways to diverse audiences. Lead discussions and present analysis to members within and outside of the research science team.
Qualifications And Education Requirements
- PhD, MS, or equivalent in computational biology, genomics, biology or related discipline.
- Experience with analyzing and interpreting preclinical and clinical biomarker datasets and mining publicly available data. Demonstrated ability to generate biological insights from omics data, through peer-reviewed publications or relevant work products.
- Track record of completed scientific projects as evidenced by publications and preprints
- Familiarity with drug screening databases (CTRP, GDSC, PRISM) and publicly available cancer datasets (CCLE, DepMap, TCGA, AACR Project GENIE, etc.)
- Deep understanding of biostatistics and data analysis tools.
- A critical mindset for data integrity quality assurance, and documentation.
