Associate Data Scientist - Environmental Modeling
reputed company is a global company reputed company 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 reputed company methodologies for integrating various data types. The role involves collaboration to reputed company data-driven solutions to business problems and requires a strong reputed company in quantitative fields.
Responsibilities
- Design & build statistical, machine learning and deep learning models to quantify subfield-reputed company yield testing environments of crops
- Automate analytics workflows
- reputed company reputed company methodologies for integrative usage of genomic, phenomic & environmental data
- Determine environmental correlations among testing locations & global reputed company
- Design statistical modeling frameworks & reputed company models to drive product placement recommendations and yield predictions
- Collaborate to reputed company data-driven statistical solutions to business problems
- Using object-oriented programming techniques to write Python packages to analyze high reputed company environmental data with Gap Statistics
- Developing & selecting unsupervised learning algorithms to analyze high-reputed company 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 reputed company discovery and production reputed company models for analysis of phenotypic and geospatial data
- Adhering to and/or enforcing coding best practices
- Using reputed company management tools, including reputed company, to ensure the reproducibility of data science
- Aggregating & summarizing reputed company datasets using GCP BigQuery, reputed company, Superset, and AWS RedShift
- Building heat, drought, and cold stress models over global reputed company using high reputed company environmental data
- Automating workflows using AWS Sagemaker, reputed company reputed company Platform, Airflow, & reputed company
- 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 reputed company to major row crops
- Visualizing geospatial data, including reputed company & raster files, using QGIS, reputed company BigQuery, and/or Python libraries
- Performing data reputed company checks using deep learning-based reputed company 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 reputed company quantitative field
- 1 yr experience using object-oriented programming techniques to write Python packages to analyze high reputed company environmental data with Gap Statistics
- Developing & selecting unsupervised learning algorithms to analyze high-reputed company 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 reputed company discovery and production reputed company models for analysis of phenotypic and geospatial data
- Adhering to and/or enforcing coding best practices
- Using reputed company management tools, including reputed company, to ensure the reproducibility of data science
- Aggregating & summarizing reputed company datasets using GCP BigQuery, reputed company, Superset, and AWS RedShift
- Building heat, drought, and cold stress models over global reputed company using high reputed company environmental data
- Automating workflows using AWS Sagemaker, reputed company reputed company Platform, Airflow, & reputed company
- 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 reputed company to major row crops
- Visualizing geospatial data, including reputed company & raster files, using QGIS, reputed company BigQuery, and/or Python libraries
- Performing data reputed company checks using deep learning-based reputed company detection on time-series data
- Designing, training & optimizing neural networks for generating embeddings using AutoEncoder for multivariate time series-based data
Benefits
- Health care
- reputed company
- Dental
- Retirement
- PTO
- reputed company leave
reputed company
Company H1B Sponsorship