Data Scientist

Title : Data Scientist (Geospatial Advanced Analytics) – Overall 10yrs profiles only

Location: St. Louis, MO – Locals only. Hybrid- 3 days a week in office.

Duration : 1 Year

• Geospatial Data Scientist: This role combines data science with geospatial technology to develop models and tools that extract meaningful insights from large datasets. Professionals in this field often use machine learning to analyze spatial data for applications like predicting urban growth, analyzing transportation patterns, or assessing resource management.
• Geospatial Engineer: These engineers focus on building and maintaining scalable, cloud-based data infrastructures for geospatial applications. They lead the development of data pipelines (ETL processes) to ingest, clean, and transform large volumes of geospatial and big data for analysis.
• Senior Geospatial Analyst: Senior analysts lead complex spatial modeling projects and use advanced statistical methods to analyze geographic data. They may specialize in specific domains like urban planning, business intelligence, or environmental monitoring.
• Remote Sensing Scientist/Analyst: This role uses satellite and aerial imagery to gather and interpret data for a variety of applications, including environmental monitoring, defense, and disaster response. Advanced analytics is often used to detect changes over time, perform image classification, and extract features automatically.
• LiDAR Analyst: Using Light Detection and Ranging (LiDAR) technology, these specialists create detailed 3D models of landscapes. They apply advanced analytics to point cloud data to gain insights for applications like infrastructure planning and natural resource management.
• Geospatial Developer: These developers build the applications and tools that enable others to work with geospatial data effectively. This includes creating web mapping applications, customizing GIS software, and building location services.

Key skills for advanced geospatial analytics

• Programming: Python is essential, often with libraries like GeoPandas and Shapely, along with experience in R or SQL.
• Cloud Computing: Proficiency with platforms like Google Cloud (GCP), AWS, or Azure is crucial for managing and processing large geospatial datasets.
• Data Science & Modeling: Experience with predictive modeling, machine learning, and spatial statistics is critical for extracting meaningful insights.
• GIS Software: Expertise in professional-grade software like Esri ArcGIS (Pro, Online, Enterprise) and open-source alternatives like QGIS is required.
• Big Data Technologies: Experience with big data technologies such as Hadoop, Spark, or Kafka is valuable for handling massive datasets.