Bayer · 3 days ago

Sr Machine Learning Researcher

United States

Full-time

Remote

Senior Level

$128K/yr - $192K/yr

5+ years exp

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BiotechnologyChemical

Comp. & Benefits

No H1B

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Responsibilities

AI Model Development: Design, build, and implement advanced AI models, including deep learning algorithms, 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.

Qualification

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Machine LearningDeep LearningGenomicsPredictive ModelingBayesian StatisticsUncertainty QuantificationComputational BiologyComputer ScienceProbabilityProbabilistic ModelingData IntegrationEnvironmental ModelingNonlinear DynamicsHierarchical ModelsApplied MathematicsStakeholder EngagementDocumentation

Required

PhD with 5+ years experience (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.