Data Scientist - Innovation - PhD

Data Scientist - Innovation - PhD page is loaded## Data Scientist - Innovation - PhD locations:
Irving, TX - 75039time type:
Full time posted on:
Posted Yesterday job requisition :
JR104380
** At Caris, we understand that cancer is an ugly word—a word no one wants to hear, but one that connects us all. That’s why we’re not just transforming cancer care—we’re changing lives.
** We introduced precision medicine to the world and built an industry around the idea that every patient deserves answers as unique as their DNA. Backed by cutting-edge molecular science and AI, we ask ourselves every day:  That question drives everything we do. But our mission doesn’t stop with cancer. We're pushing the frontiers of medicine and leading a revolution in healthcare—driven by innovation, compassion, and purpose.

** Join us in our mission to improve the human condition across multiple diseases. If you're passionate about meaningful work and want to be part of something bigger than yourself, Caris is where your impact begins.
**** Position Summary
** Caris Life Sciences is seeking a creative, driven, and analytically strong Data Scientist to join the Innovation Team. This role will support the development and application of machine learning and statistical methods using next-generation sequencing (NGS) and related clinical and molecular data. The Data Scientist will contribute to assay development, biomarker research, and analytic pipeline execution under the technical guidance of senior and principal data scientists.

The successful candidate will demonstrate sound analytical judgment, scientific curiosity, and the ability to collaborate effectively within cross-functional research teams. Opportunities may exist to contribute to publications and scientific presentations as part of Caris’ ongoing innovation efforts.
** Job Responsibilities
*** Process, curate, manipulate, and analyze large and complex NGS-derived datasets to support biomarker discovery and assay development initiatives.
* Implement, test, and iterate on machine learning models and analytical workflows in alignment with established research objectives and technical direction.
* Apply statistical, machine learning, deep learning, and survival analysis techniques to analyze genomic and clinical datasets and generate interpretable results.
* Support the development and evaluation of explainable machine learning models for risk prediction and biomarker analysis.
* Assist with the integration and analysis of multi-omics data sources (e.g., WGS/WES, transcriptomics, proteomics, metabolomics) for downstream research and clinical insights.
* Execute analyses within existing analytic pipelines and contribute improvements under guidance from senior team members.
* Collaborate closely with bioinformaticians, molecular biologists, geneticists, data scientists, and software engineers to support cross-functional research goals.
* Prepare clear documentation, visualizations, and summaries of analytical results for internal stakeholders.
* Support ad hoc analytical requests with appropriate prioritization and technical oversight.
* Follow established best practices for code quality, version control, reproducibility, and data governance.
** Required Qualifications
*** PhD in Data Science, Computational Biology, Bioinformatics, Genetics/Genomics, Mathematics, Computer Science, Engineering, or a related field, with demonstrated exposure to cancer biology or translational research.
* 1-5 years of relevant working experience in bioinformatics and data science.
* Proficiency in programming in Python.
* Familiar with Linux environment and git.
* Proficient in Microsoft Office Suite, specifically Word, Excel, Outlook, and general working knowledge of Internet for business use.
** Preferred Qualifications
*** Experience with deep learning libraries such as PyTorch or Tensor Flow.
* Experience with explainable ML methods (e.g., SHAP).
* Knowledge of polygenic risk scores (PRS) and genetic risk prediction frameworks.
* Experience working with large-scale biobanks or clinical datasets (e.g., UK Biobank, All of Us, eMERGE).
* Knowledge of survival analysis and event data.
* Proficiency in the suite of…