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Data Scientist - Intermediate

Seattle, Washington | IT
Job ID: 101602
Listed on 10/20/2020

KellyMitchell matches the best IT and business talent with premier organizations nationwide. Our clients, ranging from Fortune 500 corporations to rapidly growing high-tech companies, are exceptionally served by our 1500+ IT and business consultants. Our industry is growing rapidly, and now is a great time to launch your career with the KellyMitchell team.

Data Analyst

Job Summary

The role involves working with diverse data sources, and will require the ability to munge and explore data sources to identify important datasets and features to be used for model development. Different data sources have unique data types, data structures, data quality and data limitations. If you have strong skills to deal with diverse data issues and would love to contribute your skills to solve agronomic questions, you are the one we are looking for.


  • Query data from multiple data sources
  • Data wrangling and data manipulation
  • Extract data from text formats
  • Evaluate data quality and discover data limitations
  • Build data pipelines
  • Exploratory data analysis
  • Develop features for model development
  • Conduct statistical analysis of agronomic research trials
  • Perform code reviews
  • General data analysis using statistical modeling and machine learning modeling

Desired Skills/ Experience

  • Masters Degree in Statistics, Biostatistics, Data Science, Applied Mathematics, Physics, Engineering or related highly quantitative discipline
  • Strong Python coding skills for data management, visualization, and basic modeling (strong experience and knowledge with standard data science packages such as numpy, pandas, matplotlib, seaborn, sklearn)
  • Proven experience in data querying and data summarization
  • Experience with Natural Language Processing
  • Experience building data pipelines
  • Fast learner and follows project instructions
  • Strong communication skills for effective interactions with business stakeholders as well as peer groups and team members
  • Preferred Qualifications:
  • Applied experience with agricultural science and/or agricultural datasets
  • Statistical and/or machine learning experience
  • Experience in SQL, PySpark, or SparklyR/Spark