Research Scientist - Computational Biologist

Elanco•Indianapolis, IN

2d•Hybrid

About The Position

As a Research Scientist on Elanco's Discovery Research team, you will be at the forefront of the company's mission to deliver innovative biologics that improve animal health. You will leverage and develop state-of-the-art AI and structural modeling tools to predict, design, and optimize therapeutic antibodies and proteins. Partnering closely with the principal Computational Scientist and wet-lab teams, you will directly impact the speed and success of the pipeline, helping to create the next generation of animal health therapies. This role offers a unique opportunity to grow your skills in a rapidly evolving field while contributing to tangible, real-world outcomes.

Requirements

Ph.D. in Computational Biology, Bioinformatics, Structural Biology, Computer Science, or a related field.
0-3 years of post-graduate experience applying computational methods to biological problems.
A strong foundation in structural bioinformatics and AI/ML modeling, with proficiency in a programming language like Python, and the communication skills to work effectively in a cross-functional scientific environment.

Nice To Haves

Experience with de novo protein design tools, such as diffusion models or physics-based approaches.
A track record of applying computational tools to solve biological problems, evidenced by publications or significant project contributions.
Familiarity with cloud computing environments (e.g., AWS, GCP) and MLOps principles.
Experience in antibody or protein engineering and a foundational understanding of immunology.
A proactive mindset with a passion for staying current with the rapidly evolving AI landscape and a motivation to test and evaluate new methodologies.

Responsibilities

Utilize and develop AI-driven tools (e.g., AlphaFold, Rosetta) to predict and design high-resolution structures of antibodies and other proteins, directly supporting the therapeutic biologics pipeline.
Apply generative AI and structural modeling techniques to design novel antibody and protein modalities from scratch, optimizing them for high-precision epitope targeting, affinity, and specificity.
Proactively identify and engineer out potential liabilities (e.g., aggregation, immunogenicity) and train novel structure-based ML architectures to enhance the developability of biologic candidates.
Partner closely with wet-lab scientists to analyze experimental data, validate computational predictions, and rapidly iterate on designs to accelerate antibody selection and optimization.
Stay current with the evolving AI landscape by evaluating new tools, maintaining thorough documentation, and effectively communicating complex findings to cross-functional teams.