Position Summary Caris Life Sciences is seeking a talented Data Scientist - Computational Biology to leverage one of the world's largest multi-modal cancer datasets to develop new insights into cancer biology and improve patient outcomes. This role supports the development of novel precision medicine diagnostics and therapies by working deeply with large-scale clinical cancer genomics data. Working closely with machine learning experts, clinicians, and other computational biologists, this position contributes to advancing Caris' clinical diagnostic platform and impacts hundreds of thousands of patients annually who rely on Caris testing to identify effective therapies from their cancer genomes.
Job Responsibilities - Analyze Caris' large-scale genomics and transcriptomics datasets to generate novel discoveries in cancer biology.
- In collaboration with machine learning experts, apply multi-modal modeling approaches integrating RNA-seq expression data with mutations, copy number alterations, fusions, protein markers, and clinical metadata.
- Translate analytical findings into improvements to the Caris clinical diagnostic platform to enhance treatment prediction and clinical insight.
- Publish results in peer-reviewed journals and present findings at scientific conferences and internal forums.
- Support collaborations with biopharma partners by providing analytical expertise, developing custom analyses, and communicating results to external stakeholders.
- Stay current with advances in oncology, cancer genomics research, and emerging analytical paradigms.
Required Qualifications - Ph.D. in Computational Biology, Bioinformatics, or a related quantitative field; or M.S. degree with 3+ years of relevant experience.
- Demonstrated track record of novel discoveries in cancer genomics using large-scale, multi-modal datasets involving thousands of patients.
- Conceptual understanding of experimental genomics methods such as RNA-seq and exome sequencing, as well as molecular biology techniques.
- Understanding of statistical and machine learning techniques with hands-on experience applying modern models.
- Proficiency in Python and the scientific computing ecosystem.
- Strong written and verbal communication skills.
- Familiarity with Linux environments and Git.
- Proficient in Microsoft Office Suite including Word, Excel, Outlook, and internet tools.
Preferred Qualifications - Publishing record in peer-reviewed cancer genomics journals.
- Experience analyzing cohort-level electronic health records and real-world data.
- Experience using cloud or high-performance computing environments.
Physical Demands - Primarily office-based role requiring prolonged periods of sitting and computer use.
Training- All job-specific, safety, and compliance training are assigned based on the job functions associated with this position.
Other - This position may require periodic travel and occasional evening or weekend work.
Annual Hiring Range$125,000 - $150,000
Actual compensation offer to candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level. The pay ratio between base pay and target incentive (if applicable) will be finalized at offer.
Conditions of Employment: Individual must successfully complete pre-employment process, which includes criminal background check, drug screening, credit check ( applicable for certain positions) and reference verification.
This job description reflects management's assignment of essential functions. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities to this job at any time.
