Associate Data Scientist – Environmental Modeling for Chesterfield, MO to 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. Requires 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; and designing, training & optimizing neural networks for generating embeddings using AutoEncoder for multivariate time series-based data. Position may telecommute from home office location within reasonable commuting distance of Chesterfield, MO up to 4 days per wk. Salary Range: Employees can expect to be paid a salary between $115,000.00 to $125,000.00. Additional compensation may include a bonus or commission (if relevant).  Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc. The offered salary may vary within this range based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.  Mail resume to Cascinda Fischbeck, Bayer Research and Development Services LLC, 800 N. Lindbergh Blvd., E2NE, St. Louis, MO 63167 or email resume to careers_us@bayer.com.  Include reference code below with resume.