Part-time Student - Data Science and Analytics - Urbandale, Ia or Austin, Tx

John Deere John Deere · Industrial · Urbandale, IA +1 · Data and Analytics (CA)

Part-time student role focused on developing and evaluating AI/ML models, causal inference methods, and GenAI-enabled workflows. The role involves data preparation, building analysis tools and prototypes, and researching new AI methodologies. Experience with agentic AI systems and LLM-based applications is a plus.

What you'd actually do

  1. Explore analytical problems and develop creative, data-driven solutions using modern data science, analytics, and AI techniques.
  2. Ingest, evaluate, clean, transform, and prepare structured and unstructured data for use in algorithms, models, dashboards, and analytical solutions.
  3. Build analysis tools, prototypes, data pipelines, and dynamic visualizations to accelerate insight generation and support decision-making for internal customers and product teams.
  4. Support the development and evaluation of algorithms, machine learning models, causal inference methods, and GenAI-enabled workflows.
  5. Research new analytical, visualization, automation, and AI methodologies in collaboration with data science, engineering, agronomy, UX, and product subject matter experts.

Skills

Required

  • 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.

Nice to have

  • 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 analytics, geospatial feature engineering, remote sensing analysis, satellite imagery, or precision agriculture datasets.
  • Experience working with Databricks, Apache Spark, distributed computing, and large-scale data processing environments.
  • Experience building production-quality analytical workflows, reusable data products, or scalable data science pipelines.
  • Familiarity with cloud-based data platforms, model development environments, version control, and collaborative software development practices.
  • Experience working as part of a digital product team, including collaboration with product managers, engineers, designers, domain experts, and business stakeholders.
  • Ability to translate stakeholder needs into analytical questions, technical requirements, prototypes, and actionable insights.
  • Experience creating interactive dashboards, data applications, or visualization tools that support decision-making.
  • Knowledge of agricultural, geospatial, machine telemetry, IoT, or digital product data is a plus.

What the JD emphasized

  • GenAI-enabled workflows
  • agentic AI systems
  • AI assistants
  • workflow automation
  • LLM-based applications

Other signals

  • Develops and evaluates algorithms, machine learning models, causal inference methods, and GenAI-enabled workflows.
  • Researches new analytical, visualization, automation, and AI methodologies.
  • Experience designing, building, evaluating, or deploying agentic AI systems, AI assistants, workflow automation, or LLM-based applications.