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

Responsibilities

• 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.
• Create 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.

Requirements

• 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.

Skills

• 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 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.
• Able 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.