Senior Data Scientist

Caris Life Sciences•Papago, AZ

About The Position

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 The experienced Senior Data Scientist will support our precision medicine and biomarker discovery initiatives. This role will contribute to target and drug discovery efforts through advanced analytics, machine learning, and large-scale data processing across multi-omics and clinical datasets, and brings a strong quantitative and scientific background, excellent programming skills, and the ability to translate complex biological questions into robust analytical solutions. This role also involves technical leadership, cross-functional collaboration, and ownership of end-to-end analytics and machine learning workflows.

Requirements

Ph.D. in Mathematics, Computer Science, Engineering, Bioinformatics, Computational Biology, or a related quantitative field with strong domain knowledge in molecular biology and genomics.
Proficiency in Python (and/or R) for data analysis, statistical modeling, and machine learning in a scientific computing environment.
Hands-on experience with AWS cloud computing platform.
5+ years of relevant experience applying quantitative, statistical, and computational approaches in biotech, life sciences, or translational research.
Demonstrated experience in computational biology, including analysis and interpretation of high-throughput biological data such as genomics, transcriptomics, proteomics, epigenomics, or other multi-omics modalities.
Hands-on experience working with molecular, cellular, clinical, and/or multi-omics datasets, including data QC, normalization, integration, and downstream analysis.
Strong ability to translate biological and clinical research questions into statistically sound and computationally efficient analytical solutions.
Working knowledge of bioinformatics tools, pipelines, and data formats (e.g., FASTQ, BAM/CRAM, VCF, GTF, HDF5, or equivalent).
Proficiency with Linux and command-line workflows commonly used in bioinformatics and computational research.
Experience querying and working with complex relational and non-relational databases.
Strong verbal and written communication skills, with the ability to clearly explain complex biological and computational concepts to multidisciplinary audiences.
Team-oriented mindset with a passion for personalized medicine, data-driven discovery, and scientific innovation.

Nice To Haves

Experience in cancer biology, molecular biology, biomarker discovery, or diagnostic development.
Proven experience developing and validating predictive models using machine learning, deep learning, LLM and/or Agentic AI techniques.
Experience designing and maintaining relational (e.g., MySQL, PostgreSQL) and non-relational (e.g., MongoDB) databases.
Experience developing data-driven or analytics-enabled applications.
Familiarity with modern software engineering best practices, including CI/CD, containerization, microservices, and system architecture.
Knowledge of DevOps, MLOps, and/or DataOps concepts and tooling.
Demonstrated success working in multidisciplinary, cross-functional teams.

Responsibilities

Lead and contribute to scientific research efforts using advanced statistical, machine learning, and AI methodologies.
Manipulate, integrate, and analyze large, multi-source and multi-omics datasets.
Design, develop, validate, deploy, and monitor data processing and machine learning pipelines.
Develop, implement, refine, and test algorithms and workflows to meet specific project objectives.
Provide statistical support for experimental design, assay development, biomarker discovery, and diagnostic product development.
Build and deploy scalable analytics solutions, APIs, and data applications using cloud and/or HPC environments.
Author and review statistical analysis plans, protocols, and technical reports for clinical and non-clinical studies.
Collaborate closely with data engineers, data scientists, software engineers, and R&D scientists to build and maintain analytics platforms.
Lead or contribute to cross-functional and cross-team research initiatives.
Provide general informatics and analytical support for laboratory research, technology development, and clinical studies.
Respond to ad hoc data analysis requests with accuracy, rigor, and timeliness.
Adhere to coding, documentation, and quality standards; manage technical deliverables for assigned projects.
Provide technical mentorship and guidance to junior team members, as appropriate.