Senior Data Scientist - AI/ML for Precision Oncology

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

Want to help build AI models for the next generation of cancer diagnostics? The models you build here have direct line-of-sight to translational research and clinical decision-making -- work with the potential to shape how cancer is detected, profiled, and treated. As a Data Scientist on the Innovation Team, you will develop machine learning and deep learning algorithms on molecular sequencing data (WGS, WES, RNA-seq, cfDNA), design analytic pipelines for novel biomarker discovery, and tackle the most challenging problems in liquid biopsy and translational oncology research.

About The Team

The Innovation Team is a small, fast-moving R&D group within Caris Life Sciences, drawing on proprietary clinical research data that no other team in oncology can match. We work closely with bioinformaticians, molecular biologists, and clinical scientists to develop high-impact AI models with the potential to shift the landscape of clinical outcomes. You will have the freedom to lead research projects end-to-end -- from problem framing to deployment -- and to shape the methods that drive Caris' R&D agenda. In your first year, success looks like leading one or two research projects from problem framing through deployment, contributing to a peer-reviewed publication or conference submission, and helping shape methods that inform Caris' diagnostic platform.

Job Responsibilities

• Processing, manipulating, and analyzing large diverse datasets generated from NGS to develop biomarkers for cancer diagnosis, prognosis, and treatment.
• Developing novel algorithms for feature extraction and biomarker discovery from molecular sequencing data.
• Applying first-principles analysis to translate open research questions into tractable, well-defined problems.
• Applying state-of-the-art machine learning and deep learning methods to biological and clinical research questions.

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• Creating rigorous evaluation frameworks and tracking experiments systematically using tools such as MLflow or Weights & Biases.
• Authoring peer-reviewed research publications and presenting findings at scientific conferences.
  
  

Required Qualifications

• PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or a related quantitative or biological field.
• PhD recently completed, or up to approximately 2 years of post-doctoral research experience (academic or industry).
• Demonstrated work on a cancer biology or translational research problem (PhD thesis chapter, peer-reviewed publication, or postdoc / industry role).
• Hands-on experience with molecular sequencing data (e.g., WGS, WES, RNA-seq, cfDNA) including production-grade pipelines and analysis.
• Hands-on experience with generative AI -- large language models, foundation models (e.g., genomic or protein language models), or agentic workflows applied to scientific or clinical data.
• Proficiency with PyTorch and modern deep learning architectures (transformers, attention mechanisms), with demonstrated application of ML/DL to biological or clinical data.
• First-author or co-first-author peer-reviewed publications in machine learning venues (e.g., NeurIPS, ICML, ICLR) or in bioinformatics / computational biology journals.

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• Strong Python; comfortable in Linux; proficient with git and collaborative workflows.
  
  

Preferred Qualifications

• Multi-omics integration experience (genomics, transcriptomics, proteomics, methylation, etc.).
• Experience with epigenetics -- DNA methylation analysis, chromatin biology, or related.
• Interest in cell-free DNA, liquid biopsy, and next-generation early cancer diagnostics.
• Interest in novel algorithm development for biomedical signal extraction in sequencing data.
• Proficiency in cloud platforms (AWS EC2, S3, HealthOmics) and containerization (Docker).
  
  

Physical Demands

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• This role primarily involves sedentary work at a computer workstation, including extended periods of typing, reading screens, and virtual or in-person collaboration. Caris provides reasonable accommodations to qualified individuals with disabilities; candidates who need accommodation during the application or interview process are encouraged to contact Caris HR.
  
  

Training

All job-specific, safety, and compliance training are assigned based on the job functions associated with this employee.

Other

• This position is on-site in Irving, TX. The team operates on a fast-iteration research cycle that benefits from close, in-person collaboration.
• Relocation assistance may be available for qualified candidates.
  
  

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.

Caris Life Sciences is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.