Part-Time Student - Data Science and Analytics - Urbandale, IA or Austin, TX

Part-Time Student
- Data Science and Analytics

There are over 7 billion people on this planet. And by 2050, there will be 2 billion more... many moving into urban centers at an unprecedented rate. Making sure there is enough food, fiber and infrastructure for our rapidly growing world is what we're all about at John Deere. And it's why we're investing in our people and our technology like never before!

Here the world's brightest minds are tackling the world's biggest challenges. If you believe one person can make the world a better place, we'll put you to work. RIGHT NOW.

John Deere is an equal opportunity employer, including disabled & veterans.

Primary

Location:

United States (US) - Iowa
- Urbandale Function:
Data and Analytics (CA)

Title:

Part-Time Student
- Data Science and Analytics
- Urbandale, IA or Austin, TX - 122125 Onsite/Remote:
Onsite Position

The Part-Time Student Program is primarily designed to augment the Company's regular full-time staff and is for students who work in parallel to their school schedule YEAR-ROUND.

As a Part Time Student
- Data Science and Analytics for Intelligent Solutions Group Urbandale located in Urbandale, IA, or Austin, TX you will be:

• Explore analytical problems and develop creative, data-driven solutions using modern data science, analytics, and AI techniques.
• Ingest, evaluate, clean, transform, and prepare structured and unstructured data for use in algorithms, models, dashboards, and analytical solutions.
• Build analysis tools, prototypes, data pipelines, and dynamic visualizations to accelerate insight generation and support decision-making for internal customers and product teams.
• Support the development and evaluation of algorithms, machine learning models, causal inference methods, and GenAI-enabled workflows.
• Research new analytical, visualization, automation, and AI methodologies in collaboration with data science, engineering, agronomy, UX, and product subject matter experts.
• Apply data science and statistical techniques to solve business and product problems across the product lifecycle.
• Communicate findings, methodologies, assumptions, and recommendations clearly to technical and non-technical stakeholders at multiple levels.
• Work effectively in a collaborative, cross-functional environment while demonstrating curiosity, continuous learning, attention to detail, and high standards of quality.

• Graduate-level academic experience preferred, including current enrollment in a Master's or PhD program in Data Science; others may apply.
• Graduation date of Spring 2027 or later.
• Available to work during the academic year (16-20 hours weekly).
• Available to work during the summer semester (30-40 hours weekly).
• Must be registered as a full-time student at a U.S/local accredited university/college
• Cumulative GPA of 3.0 or above.
• Must be able to commute to the work location in Urbandale, IA or Austin, TX on a daily basis.
  
  NOTE:
  
  Relocation assistance is not provided.
• Strong analytical and problem-solving skills with the ability to explore ambiguous business or technical problems.
• Proficiency in Python, including experience with algorithms and data structures.
• Proficiency in SQL.
• Experience working with Generative AI tools.
• Foundational knowledge of machine learning and statistics.
• Knowledge of big data analysis, including experience or coursework with distributed data processing frameworks such as Apache Spark.
• Ability to create analytical outputs, dashboards, visualizations, or tools that help generate insights for customers and stakeholders.
• Strong communication skills, including the ability to explain technical methods and findings clearly to both technical and non-technical audiences.
• High level of attention to detail, accuracy, and ability to manage work effectively.

• Experience designing, building, evaluating, or deploying agentic AI systems, AI assistants, workflow automation, or LLM-based applications.
• Strong proficiency in causal inference, including experience with experimental design, quasi-experimental methods, treatment effect estimation, or causal modeling.
• Experience with geospatial data science, including spatial…