Role Summary
Deloitte's Prism MLOps team is seeking a Senior GIS Data Scientist to lead geospatial data operations within a modern MLOps environment, with a focus on the healthcare sector. In this role you will collect, process, and manage spatial data; build and publish maps; and perform advanced geospatial analysis that supports healthcare use cases — such as facility location planning, population health mapping, service accessibility, and epidemiological/disease-spread analysis — feeding directly into machine learning pipelines.
You will bridge deep GIS expertise (ArcGIS Enterprise, ArcGIS Pro, ArcPy, Python) with cloud-scale data engineering (GCP, BigQuery, PySpark) and modern DevOps/MLOps tooling (GitHub Actions, Docker, CI/CD pipelines, GitHub Copilot), helping the team operationalize geospatial healthcare models reliably and at scale. This is a hands-on senior role that also carries mentoring and stakeholder-facing responsibilities.
Key Responsibilities
Geospatial Analysis & Data Science
- Collect, process, and manage spatial data from multiple sources (satellite imagery, GPS, LiDAR, vector/raster datasets) and ensure data quality and accuracy.
- Perform advanced spatial analysis — site suitability, network/route analysis, catchment and accessibility analysis, proximity and overlay analysis — to support healthcare model development and business decisions.
- Apply geospatial techniques to healthcare-specific problems: healthcare facility siting, patient/population distribution mapping, service-area and coverage-gap analysis, and disease surveillance/hotspot detection.
- Design, build, and automate GIS tools, models, and geoprocessing workflows using Python and ArcPy.
- Prepare, produce, and publish high-quality maps and geospatial visualizations for analytical and operational use.
ArcGIS Platform
- Develop, configure, and administer solutions on ArcGIS Enterprise, ArcGIS Pro, and the ArcGIS API, including publishing and managing map and feature services.
- Maintain spatial databases (PostGIS/PostgreSQL and equivalents) and manage parameters for varied use cases.
MLOps, DevOps & Cloud Engineering
- Integrate geospatial datasets and features into ML pipelines within the Prism MLOps environment.
- Build scalable data processing workflows on Google Cloud Platform (GCP) using BigQuery and PySpark.
- Design and maintain CI/CD pipelines to automate build, test, and deployment of geospatial and ML workloads.
- Implement and manage automated workflows using GitHub Actions (build, test, lint, deploy, scheduled geoprocessing jobs).
- Containerize GIS tools, models, and services using Docker for consistent, reproducible deployments across environments.
- Leverage GitHub Copilot and other AI-assisted development tools to accelerate coding, testing, and documentation.
- Support model deployment, monitoring, versioning, and reproducibility of geospatial ML workloads in production.
- Handle sensitive healthcare/geospatial data in line with privacy and data-governance standards.
Collaboration & Delivery
- Track and manage work using JIRA, following agile delivery practices.
- Manage source code, branching, and reviews using Git/GitHub.
- Act as a point of contact for stakeholders; translate healthcare requirements into GIS/geospatial solutions with clear scope and estimates.
- Perform QA/QC on deliverables and mentor junior GIS developers and data scientists.
Required Skills & Qualifications
- 8+ years of professional experience in GIS, geospatial analysis, or geospatial software development.
- Domain knowledge of the healthcare sector, with the ability to apply geospatial methods to healthcare problems (facility planning, population health, accessibility, epidemiology).
- Expert-level GIS analysis, spatial analysis, and map creation.
- Strong Python programming with ArcPy (and ideally PyQGIS) for geoprocessing and automation.
- Hands-on experience with ArcGIS / ArcGIS Pro / ArcGIS Enterprise and the ArcGIS API.
- Experience with spatial databases: PostGIS, PostgreSQL (SQL Server / Oracle / MySQL a plus).
- Demonstrated problem-solving ability and a track record of process optimization and automation.
- Experience with GCP, BigQuery, and PySpark for large-scale data processing.
- Hands-on experience with CI/CD pipeline design and management.
- Proficiency with GitHub Actions for workflow automation and Git/GitHub for version control.
- Experience with Docker and containerization of applications and services.
- Experience using GitHub Copilot or similar AI-assisted development tooling.
- Familiarity with JIRA and agile/cloud delivery workflows.
- Bachelor's or Master's in Remote Sensing, Geomatics, Geoinformatics, Computer Science, or a related field.
Preferred / Nice-to-Have
- Prior experience delivering healthcare or public-health GIS projects.
- Familiarity with healthcare data standards and privacy/governance considerations (e.g., handling of sensitive/PHI-adjacent data).
- Experience with container orchestration (Kubernetes) and infrastructure-as-code.
- Experience with QGIS, GeoServer, Global Mapper, GDAL, Leaflet, Mapbox, OpenLayers.
- Image analytics and classification LiDAR / point-cloud processing.
- Prior cloud experience with GCP, AWS or Azure.
- Experience mentoring teams and owning end-to-end project delivery.
What You'll Bring to Prism MLOps
A rare combination of deep GIS craftsmanship, healthcare-domain insight, and modern MLOps engineering — able to move from raw spatial data collection and map production all the way to containerized, automated, production-grade geospatial ML pipelines that drive better healthcare outcomes, while raising the capability of the team around you.
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