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Machine Learning Geospatial Jobs in Arizona (NOW HIRING)

Staffingine LLC

AI ML Engineer

Phoenix, AZ · On-site

$113K - $136K/yr

Statistical Modeling Machine Learning Regression Classification Clustering Time Series Forecasting ... Familiarity with geospatial data and mapping tools * Knowledge of Monte Carlo simulations and ...

Pae

... Machine Learning (AI/ML) to automate and enhance analysis, and the utilization of cloud-based architectures for geospatial data storage and processing. * Must have 8 years of experience as a 350G ...

Motorola

Senior DevOps Engineer (US REMOTE)

Phoenix, AZ · Remote

$140K - $170K/yr

The solutions we create apply exciting technologies such as geospatial visualization and analytics ... Experience with AI/machine learning technologies is strongly preferred. * Familiarity with TCP/IP ...

All JobsMachine Learning Geospatial JobsMachine Learning Geospatial Jobs in Arizona

Machine Learning Geospatial information

What does a Machine Learning Geospatial professional do?

A Machine Learning Geospatial professional uses machine learning techniques to analyze and interpret geospatial data, such as satellite imagery, maps, and GPS data. Their work involves building and training models to detect patterns, make predictions, and solve spatial problems in fields like agriculture, urban planning, disaster response, and environmental monitoring. These professionals often collaborate with data scientists and GIS (Geographic Information Systems) specialists to extract actionable insights from large and complex geospatial datasets. Their skills are crucial for automating tasks such as image classification, land cover mapping, and object detection in geographic contexts.

What are the key skills and qualifications needed to thrive as a Machine Learning Geospatial professional?

To thrive as a Machine Learning Geospatial specialist, you need a strong background in machine learning, geospatial analysis, programming (Python, R), and a relevant degree in computer science, geography, or a related field. Familiarity with GIS software (e.g., ArcGIS, QGIS), remote sensing tools, and cloud platforms like Google Earth Engine or AWS is typically required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with multidisciplinary teams. These skills and qualities are crucial for developing accurate geospatial models and delivering actionable insights from complex spatial data.

What are some common challenges faced by Machine Learning Geospatial professionals when integrating spatial data into predictive models?

Machine Learning Geospatial professionals often encounter challenges such as managing large and complex spatial datasets, ensuring data quality and consistency, and handling spatial autocorrelation that can bias model results. Additionally, integrating diverse data sources—like satellite imagery, sensor data, and GIS layers—requires advanced pre-processing and domain knowledge. Collaborating with GIS analysts and domain experts is usually essential to develop robust models that provide actionable insights.

What is the difference between Machine Learning Geospatial vs GIS Analyst?

AspectMachine Learning GeospatialGIS Analyst
Required CredentialsBachelor's or higher in Computer Science, Data Science, or related fields; knowledge of machine learning and geospatial dataBachelor's in Geography, GIS, or related fields; proficiency in GIS software
Work EnvironmentTech companies, data science teams, research institutionsGovernment agencies, urban planning, environmental firms
Industry UsageData-driven geospatial analysis, predictive modeling, AI applicationsMapping, spatial data management, spatial analysis

Machine Learning Geospatial professionals focus on applying machine learning techniques to analyze geospatial data, often working with large datasets and developing predictive models. GIS Analysts primarily handle spatial data management, mapping, and analysis using GIS software. While both roles work with geospatial data, Machine Learning Geospatial roles emphasize data science and AI, whereas GIS Analysts focus on spatial information management and visualization.

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What cities in Arizona are hiring for Machine Learning Geospatial jobs?

Cities in Arizona with the most Machine Learning Geospatial job openings:

Machine Learning Geospatial jobs near you

AI ML Engineer

Staffingine LLC

Phoenix, AZ • On-site

$113K - $136K/yr

Contractor

Re-posted 29 days ago

Job description

Job Title: AI ML Engineer
Job Location: Charlotte, NC / Phoenix, AZ
Job Type: Contract

Job Description:

  • Programming Languages Python Pandas NumPy Scikitlearn SQL
  • Statistical Modeling Machine Learning Regression Classification Clustering Time Series Forecasting
  • Data Visualization Power BI Tableau Matplotlib Seaborn
  • Big Data Technologies Spark Hadoop basic understanding
  • Cloud Platforms Azure or AWS especially for data pipelines and model deployment
  • Data Engineering ETL processes data wrangling and feature engineering
  • Insurance Reinsurance Domain Knowledge Exposure to actuarial models risk assessment and claims analytics

Technical Skills:

  • R or SAS for statistical analysis
  • Experience with Natural Language Processing NLP
  • Familiarity with geospatial data and mapping tools
  • Knowledge of Monte Carlo simulations and stochastic modeling
  • Experience with Git and CICD pipelines
  • Exposure to regulatory frameworks Solvency II IFRS 17

Soft Skills:

  • Strong problem solving and critical thinking abilities
  • Excellent communication and storytelling skills for nontechnical stakeholders
  • Collaborative mindset with cross functional teams actuarial underwriting IT
  • Ability to manage multiple projects and prioritize effectively
  • Curiosity and continuous learning attitude
  • High attention to detail and data integrity

Qualifying Questions:

  • Can you describe a project where you applied machine learning to solve a business problem in the insurance or financial domain
  • How do you ensure the quality and reliability of your data before building models
  • Have you worked with actuarial teams or underwriting departments before If yes how did you contribute to their decision making process

Mandatory Skills : AI/GenAI Research