Part-Time Student - Data Science and Analytics - Austin, TX or Urbandale, IA
Deere · Austin, Texas, US, 78701 · $15 - $40 hourly based on published rates for business function and education level.
- Paid
- Part-Time Student
About the role
Your Responsibilities
As a Part Time Student - Data Science and Analytics for Deere & Company World Headquarters located in Austin TX or Urbandale IA, you will:
- Lead and/or support key analytics projects as a part of John Deere's Intelligent Solutions Group.
- Utilize analytics techniques to clean, preprocess, explore, and analyze data sets and extract actionable insights for advanced sensing projects
- Collaborate in a fun, friendly, and fast-paced team environment across disciplines and with key stakeholders to deliver data-driven solutions for the business
- Present your findings in meetings and translate your results into reports and presentations
What Skills You Need
- Pursuing a Bachelor's, Master's, or PhD Degree in Agriculture, Computer Science, Data Analytics, Data Science, Engineering, Math, Statistics, or a related quantitative field; others may apply
- Must be registered as a full-time student at a U.S. accredited college/university
- Graduation date of December 2027 or later
- Cumulative GPA of 3.0 or above
- Available to work during the academic year 16-20 hours/weekly during the business hours of 6 AM to 6 PM
- Available to work during the summer semester 32-40 hours/weekly during the business hours of 6 AM to 6 PM
- Proficient with Python and SQL
- Knowledge of applied operations research/machine learning/deep learning techniques
- Ability to work with structured/unstructured data
- Knowledge of working with AI tools for coding
- Knowledge of Gurobi, GitHub, VSCode, Databricks
- Committed to working in an Agile environment
- Must be able to commute to the work location in Austin, TX or Urbandale IA on a regular basis
What Makes You Stand Out
- Domain knowledge in Crop Science, Soil Science, or Agronomics
- Applied knowledge of OR algorithms
- Pursuing a PHD in Industrial Engineering/Operation Research, Computer Science, Data Science