Description
At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility.
You will enable data-driven decisions on target selection, experimental design, and editing outcome interpretation by developing reproducible analyses, predictive models, and clear visualisations.
Your Tasks and Responsibilities
The primary responsibilities of this role are to:
- Build and maintain reproducible pipelines and integrated datasets to evaluate and optimise 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;
- Analyse large-scale sequencing datasets to characterise editing outcomes, fidelity, and error modes, and generate clear visualisations 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.
Who You Are
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- 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 analysing genome editing NGS datasets and interpreting results in the context of CRISPR-Cas and related editing technologies;
- Knowledge of statistical methods for analysing editing precision and efficiency, and ability to clearly communicate underlying assumptions and limitations;
- Experience with plant functional genomics analyses 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.
Preferred Qualifications:
- Experience applying GenAI and large language models to scientific workflows;
- Background in protein structure prediction and analysis, protein engineering, and protein–nucleic acid interactions;
- Experience with RNA structure analysis and/or molecular simulation methods;
- Experience with chromatin accessibility and structure analysis and advanced machine learning approaches for editing-tool optimisation;
- Knowledge of DNA repair pathways and their influence on editing outcomes;
- Experience with HPC-scale computing and optimisation methods for experimental design.
Employees can expect to be paid a salary between $97,120.00 - $145,680.00. Additional compensation may include a bonus or incentive compensation. Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.