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.

Data Scientist ML Breeding Technology

Key Tasks:

• Develop state of the art AI-assisted genetic discovery tools to uncover novel genetics with optimal phenotypic performance by leveraging 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;
• Develop models for assembling genomes of high-throughput skim-seq for tens of thousands of crop genotypes;
• Analyze complex breeding technology strategies and develop visualizations and reports with Research and Development (R&D) teams;
• Identify how complex genetic interactions lead to observed phenotypes in an efficient and interpretable manner and determine whether these insights lead to better design and predictions for our current and future intelligent pipelines;
• 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;
• Implement Bayesian modeling frameworks on existing datasets/models and expand to more extensive data and improve Bayesian or AI/ML models;
• Work on a team as an individual contributor with other Data Scientists and builds cross functional relationships between Crop Science, Pharma, and Consumer Health divisions to collaboratively partner and effectively network within the Data Science Community

Qualifications

• Bachelors degree
• Educational preparation or applied experience in at least one of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, or other related quantitative discipline.

Preferred

• 5 years of experience or Masters degree with 2 years experience or PhD.