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Spatial Data Jobs in Michigan (NOW HIRING)

SAS, R, Python, Data visualization, Data curation/transformation, Spatial epidemiologic analysis, Geocoding and GIS mapping, epidemiological research-based computational modeling and simulation ...

... spatial sound design. • Proven experience implementing and reusing design systems for XR products. • Data visualization and dashboard design for enterprise/immersive contexts. • Basic 3D ...

SAS, R, Python, Data visualization, Data curation/transformation, Spatial epidemiologic analysis, Geocoding and GIS mapping, epidemiological research-based computational modeling and simulation ...

Skilled at teaching map interpretation, spatial analysis, and geographic reasoning. Guides students through reading topographic and thematic maps, analyzing population data and migration patterns ...

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Spatial Data information

See Michigan salary details

$38.8K

$113.1K

$154.7K

How much do spatial data jobs pay per year?

As of Jun 22, 2026, the average yearly pay for spatial data in Michigan is $113,060.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,800.00 and $119,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Spatial Data Analyst, and why are they important?

To excel as a Spatial Data Analyst, you need a strong background in geography, GIS, data analysis, and a relevant degree such as geography, environmental science, or computer science. Proficiency in GIS software (e.g., ArcGIS, QGIS), spatial databases (like PostGIS), and programming languages such as Python or R is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills distinguish top performers in this field. These competencies are essential for accurately interpreting spatial data, generating actionable insights, and effectively sharing findings with stakeholders.

What are some typical challenges faced by spatial data analysts when working with large geospatial datasets?

Spatial data analysts often encounter challenges related to data quality and integration when working with large geospatial datasets. Issues such as inconsistent data formats, missing metadata, and varying spatial resolutions can complicate analysis. Additionally, managing the computational load of processing and visualizing large, complex datasets may require specialized software and robust hardware. Collaborating closely with GIS specialists, IT teams, and data engineers helps to address these challenges and ensure reliable results.

What is the difference between Spatial Data vs GIS Analyst?

AspectSpatial DataGIS Analyst
Required CredentialsGIS certifications, degrees in geography, GIS, or related fieldsGIS certifications, degrees in geography, GIS, or related fields
Work EnvironmentData collection, database management, mapping softwareData analysis, map creation, spatial problem-solving
Employer & Industry UsageUsed by GIS professionals, urban planners, environmental agenciesEmployed in government, consulting firms, environmental organizations
Search & Comparison IntentUnderstanding data types, data managementAnalyzing spatial data, creating maps, reports

Spatial Data refers to the raw geographic information used in mapping and analysis, while a GIS Analyst actively interprets, analyzes, and visualizes this data to support decision-making. Both roles require similar credentials and are integral to GIS projects, but Spatial Data is the foundational information, whereas GIS Analysts focus on applying that data to solve spatial problems.

What is spatial data?

Spatial data, also known as geospatial data, refers to information about the physical location and shape of objects on Earth. This data is usually stored as coordinates and topology and can represent features such as buildings, roads, rivers, or even entire countries. Spatial data is used in mapping, geographic information systems (GIS), urban planning, environmental studies, and various other fields to analyze locations, patterns, and relationships. It can be stored in formats like vector (points, lines, polygons) or raster (grids, images). Understanding spatial data is essential for making informed decisions based on geographic information.
What job categories do people searching Spatial Data jobs in Michigan look for? The top searched job categories for Spatial Data jobs in Michigan are:
Senior Perception Engineer - Computer Vision & 3D Deep Learning IRC295016

Senior Perception Engineer - Computer Vision & 3D Deep Learning IRC295016

GlobalLogic

Northville, MI • On-site, Remote

$110K - $120K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 16 days ago


GlobalLogic rating

7.5

Company rating: 7.5 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

123rd of 191 rated software companies


Job description

Description
Note: GlobalLogic estimates the starting pay range for this role to be performed onsite in Detroit, MI is $110,000 - $120,000 and reflects base salary only. This pay range is provided as a good faith estimate and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.
Requirements
Strong expertise in computer vision and deep learning for object detection and segmentation tasks.
Proficiency in deep learning frameworks (PyTorch and TensorFlow) with hands-on experience implementing detection models (YOLO, Faster R-CNN, SSD, RetinaNet, Detectron, etc.).
Extensive experience with OpenCV for image processing and computer vision applications.
Solid background in 3D perception using LiDAR point clouds; proficiency with PCL and Open3D libraries.
Familiarity with LiDAR-specific deep learning models such as PointNet, PointNet++, VoxelNet, and other point cloud neural network architectures.
Experience in developing and improving perception models for adverse weather conditions (rain, snow, fog) including domain adaptation and robust feature extraction techniques.
Experience with sensor fusion techniques for combining camera and LiDAR data streams.
Strong programming skills in Python and C++ for algorithm development and optimization.
Experience with model optimization techniques for real-time inference.
Familiarity with 3D geometry, coordinate transformations, and spatial data processing.
Knowledge of evaluation metrics for object detection and tracking systems (mAP, IoU, custom metrics, etc.).
Job responsibilities
Design and implement computer vision algorithms for object detection and segmentation using camera and LiDAR data fusion.
Develop deep learning models for 2D and 3D object detection, including implementation and optimization of YOLO, Faster R-CNN, SSD, and transformer-based architectures.
Create and optimize LiDAR point cloud processing pipelines using PCL and Open3D for 3D object detection and segmentation.
Implement sensor fusion techniques to combine camera and LiDAR data for enhanced object detection accuracy.
Develop instance and semantic segmentation algorithms using state-of-the-art models like Mask R-CNN, U-Net, and DeepLab.
Implement and optimize deep learning models specifically designed for LiDAR point clouds, including PointNet, PointNet++, and other 3D neural network architectures.
Develop robust perception algorithms that maintain performance in adverse weather conditions such as rain, snow, fog, and low-light scenarios.
Build and maintain computer vision pipelines using OpenCV for image preprocessing, feature extraction, and geometric transformations.
Design and implement multi-object tracking systems using Kalman filtering, SORT, and DeepSORT algorithms.
Work with ROS2 for integration and deployment of perception algorithms.
Optimize deep learning models for edge deployment and real-time inference performance.
Develop robust evaluation metrics and testing frameworks for object detection systems.
Collaborate with cross-functional teams to integrate perception algorithms into larger autonomous systems.
Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.
What we offer
Exciting Projects:Come take your place at the forefront of digital transformation! With clients across all industries and sectors, we offer an opportunity to work on market-defining products using the latest technologies.
Collaborative Environment: You can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment - or even abroad in one of our global centers or client facilities!
Work-Life Balance:GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules and opportunities to work from home.
Professional Development:We provide continuing education classes, professional certification and training (technical, soft skills, language, and communication skills) to help you realize your professional goals. Being part of a global organization, there are additional learning opportunities through international knowledge exchanges.
Excellent Benefits:We provide our employees with competitive salaries, health and life insurance, short-term and long-term disability insurance, a matched contribution 401K plan, flexible spending accounts, and PTO and holidays
About GlobalLogic
GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world's largest and most forward-thinking companies. Since 2000, we've been at the forefront of the digital revolution - helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.

What GlobalLogic employees say

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