Scientist, Cell Therapy Discovery

Role Summary

Scientist with expertise in Computational Biology applied to Cellular Therapy, Immunology, and/or Immuno-Oncology. Develop advanced computational models using large-scale omic datasets, design and conduct laboratory experiments, and generate and interpret scientific data in a collaborative environment. Based in Gaithersburg, MD.

Responsibilities

• Plan, conduct, and troubleshoot discovery research experiments within a team environment.
• Assess and report data with a clear understanding of its implications within the overall context of the project; independently formulate conclusions and determine future experiments.
• Design, develop, and deploy AI-driven machine learning and deep learning models to extract knowledge and insights from structured and unstructured data sources relevant to the development of new therapies.
• Provide technical instruction to the team and allocate administrative work where possible.
• Work collaboratively in a matrix environment.
• Prepare and deliver presentations within the Cell Therapy Unit and across other functions.
• Ensure own work, and work of peers, are compliant with relevant internal AstraZeneca standards and external regulations.

Qualifications

• Required: BS in relevant disciplines or equivalent experience (e.g., Bioinformatics, Systems Biology, Applied Mathematics, Statistics, Data Science, Computer Science).
• Required: Minimum of 2 years of experience in Cellular Therapy, Immunology, Immuno-Oncology, Computational Biology, and/or Data Science.
• Required: Proficiency in Python and/or R.
• Required: Prior experience in UNIX-based systems, high-performance computing (e.g., SLURM), and large-scale data management.
• Required: Expertise in omics technologies, capable of processing large-scale biological datasets (e.g., single-cell/spatial transcriptomics, in-house proprietary data sources such as in vitro or in vivo data).
• Required: Deep understanding of AI, predictive modeling, and statistical methods applied to training machine learning models.
• Required: Hands-on expertise in T-cell functional characterization assays such as in vitro cytotoxicity, proliferation, cytokine measurement, and multi-parameter flow cytometry.
• Required: Ability and motivation to drive projects to completion in a team environment.

Skills

• AI, machine learning, and deep learning model development
• Omics data analysis (single-cell, spatial transcriptomics)
• Programming: Python, R
• Data management and HPC environments (e.g., SLURM)
• Laboratory techniques related to T-cell assays
• Strong collaboration and communication in matrix organizations

Education

• Bachelor‚Äôs degree in a relevant discipline or equivalent experience as listed in Qualifications