This role will primarily be focused on supporting the development of agricultural and greenhouse data that include automation and climate controls and develop the pipeline and support the transition of research models and data into production code. The work would also be focused on further developing the analysis pipeline.

This position is expected to interact with various functions within the company, including data analytics, science, engineering, and field data. Attention to detail, intellectual curiosity, and clear technical communication are key for the success of this role.

Required Skills / Experience:

• BA/BS in Computer Science, Statistics, Physics, Applied Math, Agronomy, Engineering or a related quantitative discipline with at least a year of data analytics or data science experience.

• Knowledge and hands-on experience with machine learning techniques and statistical concepts (Statistical tests, Supervised and Unsupervised learning techniques used for clustering, classification, regression)
• Scaling machine learning modeling system tools to high volume centralized systems
• Strong ability to develop and debug in Python

• Strong skill using Python  or R (eg. pandas, numpy, matplotlib / seaborn, geopandas, shapely, etc.)

• Container orchestration and cluster management systems (docker, kubernetes)
• Familiarity with big-data technologies such as Hadoop, Spark, SparkML, Apache Kafka, etc

• Strong skill using SQL or PySparkdataframe manipulations
• Knowledgeable of version control, e.g. git
• Detail-oriented and capable of performing deep dives into datasets
• Basic understanding of various machine learning and statistical techniques used for predictive modeling and analysis.
• Strong communication skills to solicit information from domain experts and present results back to a technical audience

Self-starter as well as able to work as part of a team, but be able to Lead your own project