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Data Internship is a Washington-state based agtech startup, focused on empowering growers with data. Our focus is on data insight capabilities, drawing from on-farm/off-farm, online/offline data sources to enable growers to make informed management decisions. 

Almost all growers are struggling with data silos, and seek simplification and data unification aligned toward actionable ROI-driven insights. Our solution is initially focused on permanent crops - starting with apple orchards, Washington's largest crop, and enable growers to differentiate in the global economy by improving crop yield and managing costs and challenges related to labor, water, weather, and chemicals. We're building a platform to expand across the US and internationally.


This role is focused on bringing together disparate data sources from partners, organizing data into 'layers' on mapping systems, enabling growers to plan & execute decisions on labor, nutrients, & irrigation. Position is remote / home-based; preference for candidates in PST.


The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. 



  • Analyze raw data: assessing quality, cleansing, structuring for downstream processing

  • Data pipeline: advanced knowledge of Python to pull data from data/IoT partners via APIs; knowledge of Azure Data Factory a plus.

  • Database management: maintain SQL & MySQL database services

  • Data interpretation, visualization, & mapping: Manage and layers of soil, water, nutrient, yield, labor data onto API-based mapping services

  • Collaborate with engineering team to bring analytical prototypes to production

  • Generate actionable insights for business improvements



  • Enrollment in quantitative field (Statistics, Mathematics, Computer Science, Engineering, Management Information Systems, etc.)

  • Precision agriculture context and training strongly preferred.

  • At least 1  years' of experience in quantitative analytics or data modeling

  • Understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms.  

  • Fluency in a programming language (Python, R, C, C++, Java, SQL)

  • Documentation with Git tools (GitHub)

  • Preference given to students in Washington State work-study program

AgAID- Undergraduate Research Internship

The AgAID Institute’s mission is to build and foster partnerships between AI and Ag communities, and create a transdisciplinary ecosystem for technology innovation and knowledge transfer. Are you an undergraduate student looking for a cool research opportunity that allows you to work at the interface of cutting-edge Artificial Intelligence (AI) technologies and agricultural applications? We're looking for students interested in developing or applying AI tools for improved water resource modeling, improved decision making on farms, creative robotics solutions for agricultural automation, and seeing how to take the latest AI tools and algorithmic principles and put them to practice to solve real-world, challenging tasks.


Program Dates

June 6th-July 29th, 2022


Host Institutions 

Internships are located at WSU, OSU, and UC Merced. Some with remote options.

Who Should Apply

The program accepts applications from rising juniors, seniors, and outstanding sophomores who would enjoy working on interdisciplinary projects with leading experts in computer science and agricultural and are majoring in computer science/engineering, agricultural, biological, electrical engineering, crop science, life sciences, or other related areas. Students interested in machine learning, computer vision, robotics, sensors, drones, or specialty crops are strongly encouraged to apply..

Application Deadline

We will start reviewing applications on March 1st, 2022.

Program Contact

Jordan Jobe -

Find the link to the application at:

Let’s Work Together

Get in touch so we can start working together.

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