Protein Engineering Data Science Co-Op

Bayer•Chesterfield, MO

5d•$23 - $48

About The Position

Bayer is seeking a motivated Computational Co-op to support protein engineering and biological data science projects in an interdisciplinary research environment. The position develops scalable computational workflows, integrates complex biological datasets, and applies machine learning to questions in protein design, plant biology, and insect resistance. The co-op works with protein engineers, computational biologists, data scientists, and domain experts, gaining hands-on experience at the intersection of cloud computing, bioinformatics, multi-omics, machine learning, and agricultural biotechnology.

Requirements

Currently pursuing a Master's or Ph.D. degree in Bioinformatics, Computational Biology, Data Science, Chemistry, Computer Science, Biochemistry, Biophysics, Genetics, Genomics, or a related field.
Experience programming in Python, R, Bash, or a similar programming language.
Familiarity with biological data analysis, machine learning, statistics, or scientific computing.
Interest in protein engineering, structural biology, multi-omics, plant biology, or insect biology.
Strong problem-solving, documentation, data visualization, and communication skills.
Ability to work independently while collaborating effectively with scientists from multiple disciplines.

Nice To Haves

Experience with Nextflow or another scientific workflow management system.
Familiarity with AWS, cloud computing environments, Linux, containers, Git, or high-performance computing platforms.
Experience working with large-scale biological datasets and computational pipelines.
Knowledge of bioinformatics, machine learning, and predictive modeling techniques applied to biological systems.

Responsibilities

Develop, test, and document reproducible pipelines for protein engineering, computational chemistry, and computational biology workflows.
Implement data quality checks, standardized inputs and outputs, workflow monitoring, and reproducibility practices.
Integrate protein, interactome, metabolomic, phenotypic, and other biological datasets for exploratory analysis and predictive modeling.
Maintain well-documented code using collaborative software development and version control practices.
Present project progress, analytical findings, and workflow recommendations to scientific stakeholders.
Support the development of cloud-enabled Nextflow pipelines for protein structure prediction, design, scoring, and candidate prioritization.
Integrate protein interaction and metabolomic datasets to identify signals associated with plant performance.
Apply computational approaches to model insect resistance using molecular, genomic, phenotypic, and population-level data.
Collaborate with cross-functional teams to advance protein engineering, computational biology, and data science initiatives.