Associate Data Scientist - Environmental Modeling

Bayer is a global company focused on science and innovation in agriculture. They are seeking an Associate Data Scientist specializing in Environmental Modeling to design and build statistical and machine learning models for crop yield testing, automate analytics workflows, and develop methodologies for integrating various data types. The role involves collaboration to provide data-driven solutions to business problems and requires a strong foundation in quantitative fields.

Responsibilities

• Design & build statistical, machine learning and deep learning models to quantify subfield-scale yield testing environments of crops
• Automate analytics workflows
• Develop next generation methodologies for integrative usage of genomic, phenomic & environmental data
• Determine environmental correlations among testing locations & global regions
• Design statistical modeling frameworks & prediction models to drive product placement recommendations and yield predictions
• Collaborate to provide data-driven statistical solutions to business problems
• Using object-oriented programming techniques to write Python packages to analyze high dimensional environmental data with Gap Statistics
• Developing & selecting unsupervised learning algorithms to analyze high-dimensional environmental data, including K-means, agglomerative hierarchical clustering, and/or Gaussian mixture models
• Using statistical & machine learning packages, including Tensorflow, Pandas, Multiprocessing, Joblib, Numpy, SciPy, Scikit-Learn, Keras, PyTorch, PySpark, and/or Dask, to develop discovery and production ready models for analysis of phenotypic and geospatial data
• Adhering to and/or enforcing coding best practices
• Using code management tools, including GitHub, to ensure the reproducibility of data science
• Aggregating & summarizing complex datasets using GCP BigQuery, Presto, Superset, and AWS RedShift
• Building heat, drought, and cold stress models over global regions using high dimensional environmental data
• Automating workflows using AWS Sagemaker, Google Cloud Platform, Airflow, & Docker
• Performing data operations, including spatial joins, zonal statistics, & re-projecting
• Quantifying similarity scores between different environments & using distance metrics to compare multivariate time series environmental data related to major row crops
• Visualizing geospatial data, including vector & raster files, using QGIS, Google BigQuery, and/or Python libraries
• Performing data quality checks using deep learning-based anomaly detection on time-series data
• Designing, training & optimizing neural networks for generating embeddings using AutoEncoder for multivariate time series-based data

Skills

• Master's in Statistics, Mathematics, or closely related quantitative field
• 1 yr experience using object-oriented programming techniques to write Python packages to analyze high dimensional environmental data with Gap Statistics
• Developing & selecting unsupervised learning algorithms to analyze high-dimensional environmental data, including K-means, agglomerative hierarchical clustering, and/or Gaussian mixture models
• Using statistical & machine learning packages, including Tensorflow, Pandas, Multiprocessing, Joblib, Numpy, SciPy, Scikit-Learn, Keras, PyTorch, PySpark, and/or Dask, to develop discovery and production ready models for analysis of phenotypic and geospatial data
• Adhering to and/or enforcing coding best practices
• Using code management tools, including GitHub, to ensure the reproducibility of data science
• Aggregating & summarizing complex datasets using GCP BigQuery, Presto, Superset, and AWS RedShift
• Building heat, drought, and cold stress models over global regions using high dimensional environmental data
• Automating workflows using AWS Sagemaker, Google Cloud Platform, Airflow, & Docker
• Performing data operations, including spatial joins, zonal statistics, & re-projecting
• Quantifying similarity scores between different environments & using distance metrics to compare multivariate time series environmental data related to major row crops
• Visualizing geospatial data, including vector & raster files, using QGIS, Google BigQuery, and/or Python libraries
• Performing data quality checks using deep learning-based anomaly detection on time-series data
• Designing, training & optimizing neural networks for generating embeddings using AutoEncoder for multivariate time series-based data

Benefits

• Health care
• Vision
• Dental
• Retirement
• PTO
• Sick leave

Company Overview

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