Data Scientist - Innovation - PhD (Irving, TX)

Caris Life Sciences•Irving, TX

22h•Onsite

About The Position

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.

Requirements

PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, or a related field, with demonstrated exposure to cancer biology or translational research.
2+ years relevant experience (or PhD + 0-3 years), depending on scope and demonstrated impact.
Strong Python; comfortable in Linux; proficient with git and collaborative workflows.
Proficiency with PyTorch and modern deep learning architectures (transformers, attention mechanisms) with experience applying ML/DL to biological or clinical data.
Working knowledge of generative AI tools and large language models for scientific research.
Familiarity with molecular sequencing data (WGS, WES, and/or RNA-seq) and common QC/processing concepts.
Familiarity with CNV calling algorithms and related analysis tools in a sequencing context.
Interest in algorithm development for feature extraction and denoising.

Nice To Haves

Experience with cfDNA biology or liquid biopsy analysis.
Experience designing and operating agentic AI workflows for scientific research, data analysis, or pipeline automation.
Experience with foundation models, LLM-based tooling, or AI-assisted scientific workflows in a research or production setting.
Experience with DNA methylation analysis or epigenetic signal processing.
Proficiency in AWS (EC2, S3, HealthOmics); Docker/containers.
Knowledge of survival analysis and event data.
Knowledge of wet lab sequencing processes.
Track record of peer-reviewed publications in relevant fields.

Responsibilities

Processing, manipulating, and analyzing large diverse datasets generated from NGS to develop biomarkers for cancer diagnosis, prognosis, and treatment.
Developing algorithms to generate novel features and biomarkers from molecular sequencing data.
Implementing, refining, and testing analytical workflows that achieve strategic goals in molecular profiling and R&D objectives.
Utilizing state-of-the-art statistical, machine learning, deep learning, and survival analysis methods to analyze and interpret data and drive insights.
Building predictive models with both structured and unstructured datasets.
Designing and executing agentic workflows to accelerate research iteration, model development, and pipeline automation.
Creating rigorous evaluation frameworks and tracking experiments systematically using tools such as MLflow.