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. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.
Genome Editing Data Scientist
YOUR TASKS AND RESPONSIBILITIES
The primary responsibilities of this role, Genome Editing Data Scientist, are to:
- Develop state of the art AI assisted genetic discovery tools to uncover novel genetics with optimal phenotypic performance by leveraging a variety of functional genomic datasets for building advanced mathematical models and theoretically proven concepts of machine learning, Bayesian optimization, and/or graph theory;
- Train and analyze AI techniques on industry s most extensive global agriculture dataset (i.e. genetic and phenotypic data) in the world, including highly controlled and real world data
- Identify how complex genetic interactions lead to observed phenotypes using advanced statistical methods and/or AI methodologies in an efficient and interpretable manner and determine whether these insights lead to better design and predictions for our current and future intelligent pipelines that will integrate natural variation and gene edits
- Investigate the efficacy of gene regulatory networks to establish an interpretable representation tool for realizing new trait experiments and gene editing targets in corn and soy
- Be asked to lead or assist in several areas, such as: gather, curate, and quality control new data sources across Research and Development (R&D) teams · Serves as consultant to management and others within a specific area/discipline.
- Collaborates regularly, acquiring support and partnership from cross functional scientific, engineering and IT teams across the company
WHO YOU ARE
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- MS with minimum of 4 years relevant experience ·
- Educational preparation or applied experience in at least one of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Plant Genomics, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, or other related quantitative discipline
- Team player with a track record of collaborating with diverse levels and functions
- An open-minded personality with the ability to work independently
- Distinct communication skills with fluency in English, both verbal and written
- Track record in cross-functional project management with timely delivery of results; Demonstrated ability to ensure accountability for cross-functional groups.
- Demonstrated agility and flexibility conducting research to solve complex problems.
- Experience with modeling relationships between genotypes and phenotypes with advanced statistical methods and/or AI methodologies
- Experience with at least one of python, R, RShiny, SQL, or other related languages
- Experience with Nextflow, AWS cloud services or other related cloud services
Preferred Qualifications:
PhD in computer science, statistics, computational biology or related field.
YOUR APPLICATION | ||||
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Science for a better life, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. | ||||
| ||||
Bayer is an E-Verify Employer. | ||||
Location: | United States : Missouri : Chesterfield | |||
Division: | Crop Science | |||
Reference Code: | 832693 |
Contact Us | ||||
Email: | hrop_usa@bayer.com |
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