Data Scientist – Machine Learning

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: “What would I do if this patient were my mom?” 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 Data Scientist working in Machine Learning to leverage one of the world’s largest multi‑modal cancer datasets to develop novel machine learning models that integrate molecular and clinical data to advance understanding of cancer biology and improve patient outcomes. This role sits at the intersection of modern machine learning and oncology.

Working closely with machine learning scientists, computational biologists, and oncology domain experts, the successful candidate will build models spanning deep learning and statistical approaches, deploy predictive capabilities into the Caris clinical diagnostic platform, publish scientific results, and support collaborations with biopharma partners. This is a hands‑on research role in a highly collaborative environment with significant opportunity to shape scientific direction.

Job Responsibilities Design, build, and iteratively refine novel machine learning models using modern architectures and classical statistical methods to address translational oncology questions.

Develop and apply multi‑modal modeling approaches integrating RNA‑seq expression data with mutations, copy number alterations, fusions, protein markers, and clinical metadata.

Translate model outputs into improvements on the Caris clinical diagnostic platform to support improved treatment predictions.

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 machine learning research, tools, architectures, and emerging development paradigms.

Required Qualifications Ph.D. in Computer Science, Computational Biology, Applied Mathematics, or a related quantitative field; or M.S. degree with 3+ years of relevant professional experience.

Deep familiarity with modern machine learning approaches including representation learning, attention‑based architectures, foundation models, and self‑supervised learning.

Working knowledge of statistical modeling concepts relevant to clinical data, including generalized linear models, survival analysis, and Bayesian methods.

Demonstrated experience building and applying novel machine learning models beyond off‑the‑shelf solutions.

Proficiency in Python and the scientific computing ecosystem (PyTorch or Tensor Flow, scikit‑learn, pandas, Num Py, Sci Py).Strong written and verbal communication skills.

Familiarity with Linux environments and Git.

Proficient in Microsoft Office Suite including Word, Excel, Outlook, and business internet tools.

Preferred Qualifications Understanding of cancer and molecular biology with experience using large‑scale genomics datasets.

Peer‑reviewed publications in machine learning or computational biology.

Experience with computer vision for digital pathology

Experience with natural language processing of EHR or real‑world data.

Experience deploying models in cloud environments and MLOps practices.

Physical Demands Primarily office‑based role requiring extended periods of sitting and computer use.

Training All job‑specific, safety, and compliance training is assigned based on job functions.

Other May require…