Senior Scientist, Antibody Discovery & Data Science

AstraZeneca•Gaithersburg, MD

1d•$116,284 - $174,426•Onsite

About The Position

Are you ready to turn complex, multimodal discovery data into decisions that speed novel antibodies to patients? In this role, you will combine deep scientific understanding with advanced analytics to shape how we discover and optimize biologics—advancing the right candidates faster with smarter experiments and clearer insight. You will join a multidisciplinary discovery platform in Gaithersburg, partnering closely with discovery scientists, automation engineers and computational biologists. Together, you will transform in vivo and in vitro screening outputs, NGS, structural and developability data into integrated, analysis-ready insights that guide engineering and screening strategies.

Requirements

Ph.D. in Biology, Biotechnology, Computational Biology, Bioinformatics, Data Science, Biomedical Engineering or related field with 0+ years’ experience; M.S. with 5+ years’ experience; B.S. with 10+ years’ experience
Background in antibody discovery, protein engineering, biologics engineering, or developability
Experience applying data science, statistical modeling, or machine learning to biological or biomedical problems
Strong programming skills in Python, statistics and experience with scientific computing and ML tools.
Experience working with complex, multi-source datasets including NGS, and building reproducible analysis workflows
Ability to work directly with experimental scientists to solve real R&D challenges
Demonstrated experience with biological, biomedical, or experimental R&D data.
Strong communication skills and ability to work in a multidisciplinary environment.
Excellent communication and data presentation skills.

Nice To Haves

Critical thinking to formulate hypotheses and design analyses to test them
Proactive drive to identify key questions and help teams find the answers
Curiosity to dive into data generated across diverse discovery teams
Ability to influence experimental strategy, not only analyze results
Willingness to mentor others and shape team standards

Responsibilities

Run campaigns across in vivo and in vitro/display platforms to generate broad, sequence-diverse hit sets and drive progression of high-quality leads.
Build robust, reproducible pipelines for data automation; integrate sequence, assay, structural, developability, predictive and NGS data into coherent, analysis-ready datasets that accelerate decisions.
Develop statistical analyses that guide antibody engineering, screening, and candidate prioritization with measurable gains in speed and quality.
Define data needs with experimental teams, improve experimental design, and translate results into clear next steps that reduce cycle times and de-risk choices.
Create intuitive tools and visual analytics that turn complexity into clear hypotheses and empower bench scientists to leverage outputs independently.
Design iterative cycles that link computational predictions to high-throughput experimental validation, continuously improving model performance and candidate quality.
Present findings, codify best practices, and influence platform strategy to scale successful approaches across programs.