Data Scientist | Location: Minneapolis, MN

Hi,
 
Please share resume.
 

 

 

Data Scientist | Location: Minneapolis, MN 

 

Responsibilities:

The data science engineer will be a critical part of a small team tasked with advanced analytics related to sustainable agriculture and large-scale crop operations. In this role, you will:

  • Provide technical expertise to a team strong in agronomic sciences
  • Leverage statistical analysis and machine learning to support informed, data-driven decision making
  • Incrementally improve a data science framework over time leveraging sound software engineering principals, automation, and best practices in ML Ops
  • Play a leading technical role on the team spanning the entire data science lifecycle
  • Collaborate and communicate effectively across business functions to support data science initiatives
  • Innovate, explore, and practice the art of the possible to uncover valuable data science opportunities and bring them to fruition

Requirements:

  • Bachelor’s degree in data science, computer science, or related technical field
  • 5+ years of experience in statistical analysis and machine learning within the Python ecosystem including TensorFlow, PyTorch, pandas, NumPy, and sklearn
  • 3+ years of experience creating ML pipelines incorporating both structured and unstructured data
  • 3+ years of experience working with various data sources including diverse file types, web service APIs, relational databases, and NoSQL
  • 3+ years of experience implementing data science solutions on AWS, Google, or Azure cloud
  • Experience with supervised and unsupervised learning techniques as well as classification, regression, and anomaly detection problems
  • Broad understanding of machine learning including model training, hyperparameter tuning, optimization, performance evaluation, inference, model interpretability, and GPU acceleration
  • Experience scaling and optimizing data workloads on distributed systems
  • Experience working with big data file formats such as Parquet, Avro, and Iceberg
  • Proven ability to perform development at all stages in the data science lifecycle
  • Foundational understanding of software engineering, algorithms, and data structures
  • Familiarity with model lifecycle management and ML Ops concepts
  • History of continuous learning and incorporation of open data and software to solve problems
  • A passion for software, data, and AI

Preferred qualifications:

  • Master’s degree in data science or related technical field
  • Experience with data science on Databricks including Apache Spark and MLflow
  • Experience processing geospatial datasets and file formats
  • Experience working with time series data and sequence processing
  • Experience collaborating on cross-functional agile teams and presenting to key stakeholders
  • Foundational understanding of computer vision, NLP, and generative AI
  • Ability to interpret results, visualize output, and communicate data narratives to stakeholders
  • Working knowledge of regenerative agriculture, crop operations, and soil, plant, or climate sciences

 

Responsibilities:

The data science engineer will be a critical part of a small team tasked with advanced analytics related to sustainable agriculture and large-scale crop operations. In this role, you will:

  • Provide technical expertise to a team strong in agronomic sciences
  • Leverage statistical analysis and machine learning to support informed, data-driven decision making
  • Incrementally improve a data science framework over time leveraging sound software engineering principals, automation, and best practices in ML Ops
  • Play a leading technical role on the team spanning the entire data science lifecycle
  • Collaborate and communicate effectively across business functions to support data science initiatives
  • Innovate, explore, and practice the art of the possible to uncover valuable data science opportunities and bring them to fruition

Requirements:

  • Bachelor’s degree in data science, computer science, or related technical field
  • 5+ years of experience in statistical analysis and machine learning within the Python ecosystem including TensorFlow, PyTorch, pandas, NumPy, and sklearn
  • 3+ years of experience creating ML pipelines incorporating both structured and unstructured data
  • 3+ years of experience working with various data sources including diverse file types, web service APIs, relational databases, and NoSQL
  • 3+ years of experience implementing data science solutions on AWS, Google, or Azure cloud
  • Experience with supervised and unsupervised learning techniques as well as classification, regression, and anomaly detection problems
  • Broad understanding of machine learning including model training, hyperparameter tuning, optimization, performance evaluation, inference, model interpretability, and GPU acceleration
  • Experience scaling and optimizing data workloads on distributed systems
  • Experience working with big data file formats such as Parquet, Avro, and Iceberg
  • Proven ability to perform development at all stages in the data science lifecycle
  • Foundational understanding of software engineering, algorithms, and data structures
  • Familiarity with model lifecycle management and ML Ops concepts
  • History of continuous learning and incorporation of open data and software to solve problems
  • A passion for software, data, and AI

Preferred qualifications:

  • Master’s degree in data science or related technical field
  • Experience with data science on Databricks including Apache Spark and MLflow
  • Experience processing geospatial datasets and file formats
  • Experience working with time series data and sequence processing
  • Experience collaborating on cross-functional agile teams and presenting to key stakeholders
  • Foundational understanding of computer vision, NLP, and generative AI
  • Ability to interpret results, visualize output, and communicate data narratives to stakeholders
  • Working knowledge of regenerative agriculture, crop operations, and soil, plant, or climate sciences
 
 
 

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