Data Scientist - Innovation - PhD (Irving, TX)
Caris Life Sciences
June 26, 2026
On-site
Irving, TX
IT
Position Summary
- Build AI models for next-generation cancer diagnostics using molecular sequencing data to support translational research and clinical decision-making.
Job Responsibilities
- Process, manipulate, and analyze large NGS datasets to develop biomarkers for cancer diagnosis, prognosis, and treatment.
- Develop novel algorithms for feature extraction and biomarker discovery.
- Apply first-principles analysis to define tractable research problems.
- Use state-of-the-art ML/DL methods for biological and clinical research.
- Create rigorous evaluation frameworks and track experiments (e.g., MLflow, Weights & Biases).
- Author peer-reviewed publications and present at conferences.
Required Qualifications
- PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or related.
- PhD recently completed or up to ~2 years post-doctoral experience.
- Demonstrated cancer biology or translational research work.
- Hands-on experience with molecular sequencing data (WGS, WES, RNA-seq, cfDNA) and production-grade pipelines.
- Hands-on generative AI experience (LLMs/foundation models/agentic workflows) applied to scientific/clinical data.
- Proficiency in PyTorch and modern deep learning architectures (e.g., transformers/attention).
- First-author or co-first-author peer-reviewed publications in ML and/or bioinformatics/computational biology venues.
- Strong Python; comfortable in Linux; proficient with git/collaborative workflows.
Preferred Qualifications
- Multi-omics integration; epigenetics (DNA methylation/chromatin).
- Interest in cell-free DNA, liquid biopsy, and early cancer diagnostics.
- Interest in biomedical signal extraction from sequencing data.
- Cloud (AWS EC2/S3, HealthOmics) and Docker/containerization.
- Build AI models for next-generation cancer diagnostics using molecular sequencing data to support translational research and clinical decision-making.
Job Responsibilities
- Process, manipulate, and analyze large NGS datasets to develop biomarkers for cancer diagnosis, prognosis, and treatment.
- Develop novel algorithms for feature extraction and biomarker discovery.
- Apply first-principles analysis to define tractable research problems.
- Use state-of-the-art ML/DL methods for biological and clinical research.
- Create rigorous evaluation frameworks and track experiments (e.g., MLflow, Weights & Biases).
- Author peer-reviewed publications and present at conferences.
Required Qualifications
- PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or related.
- PhD recently completed or up to ~2 years post-doctoral experience.
- Demonstrated cancer biology or translational research work.
- Hands-on experience with molecular sequencing data (WGS, WES, RNA-seq, cfDNA) and production-grade pipelines.
- Hands-on generative AI experience (LLMs/foundation models/agentic workflows) applied to scientific/clinical data.
- Proficiency in PyTorch and modern deep learning architectures (e.g., transformers/attention).
- First-author or co-first-author peer-reviewed publications in ML and/or bioinformatics/computational biology venues.
- Strong Python; comfortable in Linux; proficient with git/collaborative workflows.
Preferred Qualifications
- Multi-omics integration; epigenetics (DNA methylation/chromatin).
- Interest in cell-free DNA, liquid biopsy, and early cancer diagnostics.
- Interest in biomedical signal extraction from sequencing data.
- Cloud (AWS EC2/S3, HealthOmics) and Docker/containerization.