Full Job 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. 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 – Machine Learning – Genome Modeling
YOUR TASKS AND RESPONSIBILITIES
The primary responsibilities of this role, Data Scientist – Machine Learning – Genome Modeling, are to:
- 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;
- 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;
- Assess feasibility and develop Artificial Intelligence/Machine Learning (AI/ML) for predicting phenotypes from high-resolution genetic inputs, including raw reads/k-mers to SNP markers in corn and soy;
- 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;
WHO YOU ARE
Your success will be driven by your demonstration of our LIFE (Leadership Integrity Flexibility Efficiency) values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- Ph.D. OR Master’s degree OR Bachelor’s degree;
- Experience in one or more of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, Engineering or other related quantitative discipline;
- Ability to identify common goals, build trusting relationships with internal and external stakeholders, and drive solutions;
- Proficient at identifying trends in a dynamic environment and adapting strategies to deliver the business objectives.
Preferred Qualifications:
- Six (6) or more years of relevant experience (including post undergraduate education & industrial experience);
- Track record of published work in peer-reviewed journals;
- Experience working in a cutting-edge research lab supporting cultural and technical diversity to effectively engage broad thinking and problem solving.
Domestic relocation may be available for this role.
Visa Sponsorship may be available for this role.
Position may be based in Chesterfield, Missouri, Cambridge, Massachusetts, Boston, Massachusetts, or an approved Bayer Crop Science site globally.
Position may include opportunity to work up to 100% remote.
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