Genome Editing Scientist--Bioinformatics

Bayer•Chesterfield, MO

16h•Onsite

About The Position

At Bayer, we are seeking a Genome Editing Scientist with a focus on Bioinformatics to drive data-driven decisions in target selection, experimental design, and editing outcome interpretation. This role involves developing reproducible analyses, predictive models, and clear visualizations. You will collaborate closely with molecular and structural biology colleagues to translate biological questions into computational strategies and connect predictions with experimental validation.

Requirements

PhD in bioinformatics, computational biology, genomics, or a related field, or a Master’s degree with 3+ years of additional relevant experience.
Strong programming skills in languages relevant to biological data analysis (e.g., Python, R) and ability to write maintainable, well-documented code.
Experience analyzing genome editing NGS datasets (e.g., amplicon or targeted sequencing) and interpreting results in the context of CRISPR-Cas and related editing technologies.
Knowledge of statistical methods for analyzing editing precision and efficiency, and ability to clearly communicate underlying assumptions and limitations.
Experience with plant functional genomics analyses (e.g., differential expression) and ability to integrate multi-omics signals for hypothesis generation.
Strong communication and collaboration skills, including the ability to translate computational results for diverse audiences and to work effectively across multidisciplinary teams.

Nice To Haves

Experience applying GenAI and large language models to scientific workflows, such as text mining and code or workflow acceleration, with appropriate data and privacy practices.
Background in protein structure prediction and analysis, protein engineering (structure–function), and protein–nucleic acid interactions.
Experience with RNA structure analysis and/or molecular simulation methods (e.g., molecular dynamics).
Experience with chromatin accessibility and structure analysis (e.g., ATAC-seq) and advanced machine learning approaches for editing-tool optimization.
Knowledge of DNA repair pathways and their influence on editing outcomes.
Experience with HPC-scale computing and optimization methods for experimental design, including parallelization and workflow performance tuning.

Responsibilities

Build and maintain reproducible pipelines and integrated datasets (e.g., sequence, accessibility/expression, structure) to evaluate and optimize editing tool performance across germplasm and testing systems.
Develop and apply statistical and machine learning models and metrics to quantify drivers of editing efficiency and precision and to compare tool variants across genomic contexts.
Develop computational approaches to improve the design of precise editing components, including guide and target selection and constraint-aware design.
Analyze large-scale sequencing datasets to characterize editing outcomes, fidelity, and error modes, and generate clear visualizations and decision-ready summaries.
Partner with molecular and structural biology teams to design experiments, define analysis plans, and translate multi-modal data into actionable recommendations for tool development.
Collaborate closely with genomics, functional genomics, and digital/data science stakeholders to ensure that tools, pipelines, and analyses are scalable, robust, and aligned with broader platform needs.