Data Scientist

In this role, the candidate will:

• Communicate with impact findings and methodologies to stakeholders with a variety of backgrounds.
• Work with high resolution machine and agronomic data in the development and testing of predictive models.
• Develop and deliver production-ready machine learning approaches to yield insights and recommendations from precision agriculture data.
• Define, quantify, and analyze Key Performance Indicators that define successful customer outcomes.
• Work closely with the Data Engineering teams to ensure data is stored efficiently and can support the required analytics.

Relevant skills include:

• Demonstrated competency in developing production-ready models in an Object-Oriented Programming language such as Python.
• Demonstrated competency in using data-access technologies such as SQL, Spark, Databricks, etc.
• Experience with Visualization tools such as Tableau, etc.
• Experience with Data Modeling techniques such as Normalization, data quality and coverage assessment, attribute analysis, performance management, etc.
• Experience building machine learning models such as Regression, supervised learning, unsupervised learning, probabilistic inference, natural language modeling, etc.
• Excellent communication skills. Able to effectively lead meetings, to document work for reproduction, to write persuasively, to communicate proof-of-concepts, and to effectively take notes.

What makes candidates stand-out are skills such as:

• Additional experience with other languages such as R, JavaScript, Scala, etc.
• Experience with Geospatial data search and analysis, geo-indexing techniques, vector and raster data structures.
• Experience with remote sensing and GIS tools.
• Experience with CVML
• Examples of professional work such as publications, patents, a portfolio of relevant project-work, etc.
• Familiarity with Distributed Datasets
• Experienced with a variety of data structures such as time-series, geo-tagged, text, structured, and unstructured.
• Experience with simulations such as Monte Carlo simulation, Gibbs sampling, etc.
• Experience with model validation, measuring model bias, measuring model drift, etc.
• Experience collaborating with stakeholders from disciplines such as Product, Sales, Finance, etc.
• Ability to communicate complex analytical insights in a manner which is clearly understandable by nontechnical audiences.