Senior Scientist, Protein Engineering, Computational Biology

AstraZeneca • Gaithersburg, MD

67d

About The Position

Biologics Engineering is responsible for the discovery, design, engineering, and optimization of next generation biologics to create biological drug candidates against diseases with improved safety and efficacy. The Protein Engineering & Novel Modalities Group designs, engineers, and optimizes complex therapeutics through structure-function principles while collaborating broadly across the AZ Research organization. We're constantly pushing the boundaries of science to deliver life-changing medicines to patients, with a passion for the development of in-house biologics discovery platforms and novel drug modalities to address unmet medical needs.

Requirements

• PhD in Biochemistry, Computational Biology, Artificial Intelligence or a closely related discipline.
• 2+ years of post-graduate experience applying computational approaches in antibody and/or protein engineering, including method development, data analysis, and model-based design.
• Proven expertise in protein sequence analysis, structural modeling, and applying AI/ML techniques to predict antibody features and functionality.
• Hands-on skills with scripting and programming languages (e.g., Python, R) and with advanced modeling tools for antibody engineering (e.g., Rosetta, PyRosetta, AlphaFold-Multimer, DeepChain, or similar).
• Experience building and optimizing computational workflows for large-scale protein data analysis and visualization.
• Demonstrated success collaborating within cross-functional teams, communicating computational results to guide experimental design and construct optimization.
• Track record of scientific innovation evidenced by publications, patents, or conference presentations in computational protein engineering or related fields.

Nice To Haves

• Understanding of therapeutic antibody developability, including the computational assessment of immunogenicity, stability, and potential liability prediction.
• Expertise in modeling antibody-antigen interactions and computational methods for predicting binding interactions.
• Ability to integrate data from diverse public and proprietary sources, translating computational results into actionable insights for experimental teams.
• Excellent written and verbal communication skills and effective presentation of complex scientific data to cross-functional and senior management.
• Strong analytical, problem-solving, and organizational skills, with the adaptability to work in a fast-paced, multidisciplinary pharma environment.

Responsibilities

• Leverage protein engineering, computational modeling, and AI-driven approaches to enable rational design, engineering, and optimization of complex antibody modalities.
• Analyze antibody sequence and structural data to identify and optimize bispecific and multispecific candidates using computational and AI tools.
• Design and implement computational workflows for in silico design and assessment of multispecific antibodies, including structure, specificity, and function prediction.
• Collaborate with AI/ML teams and functional hubs across the organization to advance multispecific antibody evolution.
• Partner closely with wet-lab scientists to interpret high-throughput screening data and guide experimental design.
• Prepare, analyze, and present results to internal and external stakeholders.
• Stay current with advances in data analysis methodologies and antibody engineering, contributing ideas for improved analytical strategies.

What This Job Offers

• Career Level: Senior
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees