Data Scientist - Innovation - PhD
Caris Life Sciences
August 07, 2026
On-site
Irving, TX
IT
Position Summary
- Build AI/ML models for next-generation cancer diagnostics using molecular sequencing data (WGS, WES, RNA-seq, cfDNA), designing analytic pipelines for novel biomarker discovery, and addressing challenges in liquid biopsy and translational oncology research.
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 from sequencing data.
- Apply first-principles analysis to translate research questions into tractable problems.
- Apply state-of-the-art ML and deep learning to biological/clinical questions.
- Create rigorous evaluation frameworks and track experiments (e.g., MLflow, Weights & Biases).
- Author peer-reviewed publications and present findings at scientific conferences.
Required Qualifications
- PhD in a relevant quantitative/biological field (Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or related).
- PhD completed recently or up to ~2 years post-doctoral research (academic or industry).
- Demonstrated cancer biology or translational research experience.
- Hands-on experience with molecular sequencing data and production-grade pipelines.
- Hands-on generative AI experience (LLMs, foundation models, or agentic workflows for scientific/clinical data).
- Proficiency with PyTorch and modern deep learning architectures (e.g., transformers/attention) applied to biological/clinical data.
- First-author/co-first-author peer-reviewed ML or bioinformatics/computational biology publications.
- Strong Python; comfortable in Linux; proficient with git and collaborative workflows.
Preferred Qualifications
- Multi-omics integration; epigenetics/DNA methylation/chromatin biology.
- Interest in cell-free DNA, liquid biopsy, and early cancer diagnostics.
- Interest in biomedical signal extraction algorithms.
- Cloud (AWS EC2/S3/HealthOmics) and Docker.
Application/Work Notes
- On-site in Irving, TX; relocation assistance may be available for qualified candidates.
- Build AI/ML models for next-generation cancer diagnostics using molecular sequencing data (WGS, WES, RNA-seq, cfDNA), designing analytic pipelines for novel biomarker discovery, and addressing challenges in liquid biopsy and translational oncology research.
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 from sequencing data.
- Apply first-principles analysis to translate research questions into tractable problems.
- Apply state-of-the-art ML and deep learning to biological/clinical questions.
- Create rigorous evaluation frameworks and track experiments (e.g., MLflow, Weights & Biases).
- Author peer-reviewed publications and present findings at scientific conferences.
Required Qualifications
- PhD in a relevant quantitative/biological field (Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or related).
- PhD completed recently or up to ~2 years post-doctoral research (academic or industry).
- Demonstrated cancer biology or translational research experience.
- Hands-on experience with molecular sequencing data and production-grade pipelines.
- Hands-on generative AI experience (LLMs, foundation models, or agentic workflows for scientific/clinical data).
- Proficiency with PyTorch and modern deep learning architectures (e.g., transformers/attention) applied to biological/clinical data.
- First-author/co-first-author peer-reviewed ML or bioinformatics/computational biology publications.
- Strong Python; comfortable in Linux; proficient with git and collaborative workflows.
Preferred Qualifications
- Multi-omics integration; epigenetics/DNA methylation/chromatin biology.
- Interest in cell-free DNA, liquid biopsy, and early cancer diagnostics.
- Interest in biomedical signal extraction algorithms.
- Cloud (AWS EC2/S3/HealthOmics) and Docker.
Application/Work Notes
- On-site in Irving, TX; relocation assistance may be available for qualified candidates.