Data Scientist

AstraZeneca•Santa Monica, CA

About The Position

We are seeking a highly skilled Data Scientist to lead our bioinformatic and data science efforts focused on single-cell RNA-seq (scRNA-seq) data. This role involves supporting experimental design, data processing, quality control, and downstream analysis. You will apply advanced statistical, machine learning, and AI methods to single-cell transcriptomic data to identify cell states, trajectories, biomarkers, and mechanistic insights relevant to clinical outcomes. A key aspect of this role is integrating scRNA-seq data with complementary data types such as bulk RNA-seq, genomics, and clinical metadata to support translational and clinical decision-making. You will also analyze and interpret single-cell TCR sequencing (scTCR-seq) data to characterize T-cell clonality, diversity, and clonal dynamics in clinical cell therapy studies. The integration of scRNA-seq and scTCR-seq data to link T-cell receptor repertoire features with transcriptional states, phenotypes, and clinical outcomes is crucial. You will design and implement custom analytical tools and models tailored to cell therapy and immunology research questions. Collaboration with biologists, clinicians, and cross-functional teams is essential to translate single-cell analytics into actionable insights for ongoing clinical programs. This position will also contribute to the development of analytical models for clinical NGS and single-cell data in regulated environments. Establishing and maintaining reproducible, scalable data architectures using cloud platforms and/or high-performance computing resources is expected. Building and managing code repositories, documentation, and best practices for single-cell data analysis will be part of your responsibilities. Communicating complex analytical methods and findings through clear reports, visualizations, presentations, and collaborative discussions is vital. Staying current with emerging single-cell technologies, methods, and tools, and proactively incorporating them to improve analytical approaches is also expected.

Requirements

5+ years of relevant experience with a BS/BA, or 3+ years with an MS/MA, or 1+ year with a PhD in data science, computational biology, bioengineering, or a related field, with relevant post‑graduate experience.
Deep hands‑on experience with single‑cell RNA‑seq data analysis, including normalization, batch correction, clustering, annotation, trajectory inference, and differential expression.
Strong background in NGS workflows, with emphasis on single‑cell experimental platforms and data characteristics.
Proficiency with single‑cell immune repertoire sequencing concepts, including clonotype definition, diversity metrics, and longitudinal clonal tracking.
Expertise in bioinformatics and computational biology tools and frameworks commonly used for scRNA‑seq and scTCR‑seq analysis.
Proficiency in Python and R, including development of reproducible analytical pipelines, workflows, and visualizations.
Experience working with cloud computing platforms and/or high‑performance computing clusters.
Solid understanding of statistical methods and their application to single‑cell and biomedical data.
Team‑oriented mindset with the ability to work independently in a fast‑paced, collaborative environment.
Strong communication skills, with the ability to explain complex analytical concepts to non‑experts.
Flexibility to adjust priorities and contribute beyond the initial scope as project needs evolve.

Nice To Haves

Experience in immunology, immune‑oncology, or cell therapy research is a strong plus.

Responsibilities

Lead bioinformatic and data science efforts focused on single‑cell RNA‑seq (scRNA‑seq) data, including experimental design support, data processing, quality control, and downstream analysis.
Apply advanced statistical, machine learning, and AI methods to single‑cell transcriptomic data to identify cell states, trajectories, biomarkers, and mechanistic insights relevant to clinical outcomes.
Integrate scRNA‑seq data with complementary data types (e.g., bulk RNA‑seq, genomics, clinical metadata) to support translational and clinical decision‑making.
Analyze and interpret single‑cell TCR sequencing (scTCR‑seq) data to characterize T‑cell clonality, diversity, and clonal dynamics in clinical cell therapy studies.
Integrate scRNA‑seq and scTCR‑seq data to link T‑cell receptor repertoire features with transcriptional states, phenotypes, and clinical outcomes.
Design and implement custom analytical tools and models tailored to cell therapy and immunology research questions.
Collaborate closely with biologists, clinicians, and cross‑functional teams to translate single‑cell analytics into actionable insights for ongoing clinical programs.
Contribute to the development of analytical models for clinical NGS and single‑cell data in regulated environments.
Establish and maintain reproducible, scalable data architectures using cloud platforms and/or high‑performance computing resources.
Build and manage code repositories, documentation, and best practices for single‑cell data analysis.
Communicate complex analytical methods and findings through clear reports, visualizations, presentations, and collaborative discussions.
Stay current with emerging single‑cell technologies, methods, and tools, and proactively incorporate them to improve analytical approaches.