Would prefer candidates that can work onsite in Urbandale or Austin, TX. As a Data Scientist, you will join a team leveraging petabyte-scale datasets for advanced analytics and model building to enable intelligent, automated equipment and improved decisions by farmers. Our team partners with product managers and data engineers to design, scale, and deliver full stack data science solutions. Join a passionate team making a difference by applying innovative technology to solve some of the world's biggest problems. Responsibilities: • 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. Qualifications • Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) • At least 1 - 2 years' of experience in quantitative analytics or data modeling • Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms • Fluency in a programming language (Python, C,C++, Java, SQL) Disclaimer: “U.S. Tech Solutions, Inc. is an Affirmative Action, Equal Opportunity Employer. Our employment decisions are made without regard to race, color, religion, gender, national origin, age, disability, marital status, veteran or military status, or any other legally protected status.” About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com. US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Internal ID- #25-41824