Machine Learning Researcher

Bayer•Tulsa, OK

18h•$100 - $150

About The Position

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. Machine Learning Researcher In this role, you will design, build, and deploy Artificial Intelligence models (i.e. deep learning) to analyze and connect phenomics, genomics (at all scales), environmental scenarios, and management practices for the improvement of global crops. You will work collaboratively with interdisciplinary scientists, IT and engineering professionals across the organization to analyze the industry’s most extensive and global agriculture and genomics dataset in the world, including highly controlled and real-world data. You’ll be tasked with revolutionizing how critical decisions are made via AI and building trust in the use of AI in agriculture. You will also foster changing ideas to produce sophisticated, intelligent and optimized predictive models and will work on a team as an individual contributor with other Machine Learning Researchers.

Requirements

Masters degree plus 4+ years (including PhD) Educational preparation or applied experience in at least one of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Genomics, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, or other related quantitative discipline.

Responsibilities

AI Model Development: Design, build, and implement advanced AI models, including deep learning algorithms and AI digital twins, tailored to analyze complex datasets related to phenomics, genomics, and environmental factors.
Data Integration: Collaborate with interdisciplinary teams across R&D to gather, preprocess, and integrate diverse datasets from agriculture, environment, and genomics, ensuring data quality and relevance.
Analysis and Interpretation: Conduct thorough AI analyses of industry’s most extensive global agriculture dataset to uncover insights and establish connections between phenomic traits, genomic data, and environmental conditions.
Genomics Modeling: Incorporate genomics data (e.g. high-resolution genome assemblies, k-mers, skim-seq, gene expression, etc.) into AI models to predict and optimize crop traits and resilience, enhancing overall agricultural productivity.
Environmental Modeling: Develop predictive environmental models that inform the impact of climate and weather on crops.
Predictive Risk Modeling: Develop sophisticated predictive models that inform decision-making processes and reduce risk for crop management and improvement strategies.
Collaboration: Work closely with scientists, biologists, IT professionals, and engineers to align AI initiatives with organizational goals and ensure effective implementation of models.
Trust Building: Engage with stakeholders to communicate the benefits and limitations of AI in agriculture, fostering trust and transparency in the technology.
Continuous Improvement: Stay updated on the latest advancements in AI, environmental modeling and genomics, applying new techniques and methodologies to refine models and enhance their accuracy.
Documentation and Reporting: Prepare comprehensive documentation of methodologies, findings, and model performance, and present results to both technical and non-technical audiences.
Team Contribution: Actively participate in team meetings and collaborative projects, sharing knowledge and insights with other Machine Learning Researchers to drive innovation and improvement.