Esri is seeking a Data Scientist who is passionate about leveraging machine learning and location intelligence to create data-driven solutions for customers. The role involves collaborating with clients to understand their challenges and developing analytics systems that integrate with GIS workflows. While prior GIS experience is not mandatory, the position offers the opportunity to work closely with GIS experts and gain insights into spatial data analytics. This position is based in the St. Louis, MO area and includes relocation assistance.

• 1+ years of professional experience in a similar position, supporting similar responsibilities
• Professional experience with practical machine learning experience or relevant academic/lab work
• Hands-on relevant experience in generative AI, large language models, and/or agentic systems
• Fundamental understanding of mathematical and machine learning concepts
• Applied statistics experience
• Experience developing machine learning solutions, preferably in Python
• Ability to produce compelling data visualizations to convey results
• Strong communication skills, including the ability to explain technical concepts to non-technical audiences
• US citizenship and willingness and ability to maintain a US Security Clearance
• Bachelor's degree in mathematics, statistics, computer science, physics, or a STEM related field

• Consult closely with customers to understand their needs
• Develop and pitch data science solutions by mapping business problems to machine learning or other advanced analytics approaches
• Build high-quality analytics systems that solve our customers' business problems using techniques from data mining, statistics, and machine learning
• Write clean, collaborative, and version-controlled code to process big data and streaming data from a variety of sources and types
• Perform feature engineering, model selection, and hyperparameter optimization to yield high predictive accuracy and deploy the model to production in a cloud, on-premises, or hybrid environment
• Implement best practices and patterns for geospatial machine learning and develop reusable technical components for demonstrations and rapid prototyping
• Keep up to date with the latest technology trends in machine and deep learning and incorporate them in project delivery