Geospatial Data Scientist/Geospatial AI/ML Scientist

VDart, Inc.
United States
9 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Clean Code Principles Geographic Information Systems Application Programming Interfaces (APIs) Artificial Intelligence Business Analytics Applications Data Analysis Architectural Patterns Big Data GIS Applications Python (Programming Language) Machine Learning Azure Machine Learning
+5 more
Spatial Data Infrastructures Sql Optimization Model Validation Information Technology Data Analytics

Job description

The Senior Data Scientist Geospatial is a technical leader responsible for designing, developing, and delivering advanced data science, spatial analytics, and machine learning solutions using geospatial data. This role leads complex analytics initiatives, develops scalable analytical applications and spatial data products, establishes technical standards, and mentors junior data scientists and technical team members.

The position partners closely with GIS teams, business stakeholders, and technology leaders to translate complex geospatial and business challenges into scalable, production-ready solutions that deliver measurable business impact., * Lead end-to-end data science and geospatial analytics initiatives, from problem definition and data exploration through model development, deployment, and monitoring.

  • Design and develop predictive, prescriptive, and spatial analytics models using large-scale geospatial and enterprise datasets.
  • Architect and deliver scalable GIS tools, analytical applications, spatial data products, and reusable geoprocessing capabilities.
  • Establish technical standards, development frameworks, coding practices, and architectural patterns for enterprise geospatial solutions.
  • Design and oversee spatial data pipelines, APIs, automation workflows, and geoprocessing frameworks.
  • Develop production-grade analytical tools and applications using Python and modern data science technologies.
  • Integrate GIS capabilities with cloud platforms, enterprise data systems, APIs, and AI/ML services.
  • Engineer and optimize features, analytical workflows, and machine learning models for performance, scalability, and reliability.
  • Implement model validation, testing, monitoring, and governance frameworks.
  • Develop automated spatial analysis workflows and reusable geoprocessing components.
  • Collaborate with GIS professionals, data engineers, software engineers, and business leaders to deliver enterprise-level solutions.
  • Translate complex analytical and geospatial findings into clear, actionable recommendations for technical and non-technical stakeholders.
  • Provide technical leadership and mentorship to junior and mid-level data scientists.
  • Evaluate emerging GIS, AI, ML, and data science technologies and recommend solutions aligned with business objectives.
  • Apply strong business acumen and independent problem-solving skills to address complex analytical challenges.

Requirements

  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Geography, GIS, Engineering, or another quantitative discipline; advanced degree preferred.
  • 6 10 years of professional experience in data science, analytics, machine learning, or a closely related field.
  • 6 8+ years of relevant experience for senior-level geospatial data science responsibilities.
  • Strong recent experience applying data science techniques to GIS/geospatial problems, preferably within the last 4 years.
  • Strong proficiency in Python or R, with demonstrated experience developing production-ready analytical workflows.
  • Advanced SQL skills and experience working with large-scale datasets.
  • Strong knowledge of statistical inference, machine learning, experimentation design, and predictive modeling.
  • Hands-on experience developing and deploying production-grade models, analytical applications, or data products.
  • Extensive experience with spatial analytics and geospatial data at scale.
  • Strong experience developing enterprise-grade GIS tools, applications, pipelines, and automation workflows.
  • Demonstrated ability to independently lead complex technical initiatives from concept through production.

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