Data Scientist Job Description: Would prefer candidates that can work onsite in Urbandale or Austin, TX. As a Data Scientist 1+yrs contract You will: • Communicate with impact your 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 Prog language such as Python. • Demonstrated competency in using data-access technologies such as SQL, Spark, Databricks, etc. • Experience with Visualization tools such as Tableau, Kepler.gl, 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: • Experience with Geospatial data search and analysis, geo-indexing techniques, vector and raster data structures. • Experience with remote sensing, GIS tools, and satellite imagery analysis. • Experience with CVML • Experience with advanced AI techniques and tools. • 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. • Additional experience with other languages such as Java, JavaScript, Scala, etc. • 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.