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Machine Learning Geospatial Jobs in Maryland (NOW HIRING)

... and geospatial datasets, ensuring datasets are ready for the ML training pipeline. * Lead ... Strong foundation in applied statistics, analytics, and machine learning * Proven ability to set ...

Principal Data Scientist

Germantown, MD · On-site

$134K - $181K/yr

... and geospatial datasets, ensuring datasets are ready for the ML training pipeline. * Lead ... Strong foundation in applied statistics, analytics, and machine learning * Proven ability to set ...

Principal Data Scientist

Germantown, MD · On-site

$134.33 - $181.35/hr

... geospatial datasets. * Lead classifier evaluation and validation, defining rigorous metrics and ... Solid foundation in applied statistics, analytics, and machine learning. * Proven ability to set ...

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 ...

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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 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.

What are the key skills and qualifications needed to thrive as a Machine Learning Geospatial specialist, and why are they important?

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 popular job titles related to Machine Learning Geospatial jobs in Maryland? For Machine Learning Geospatial jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Machine Learning Geospatial jobs in Maryland look for? The top searched job categories for Machine Learning Geospatial jobs in Maryland are:
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Posted 8 days ago


Job description

About Sand

Sand Technologies is a global Physical AI company using data and AI to make critical industries work better. We partner with governments, cities and enterprises to improve how essential systems operate across healthcare, water, energy, telecommunications and infrastructure.

Our work delivers proven real-world impact. We have built AI systems that help manage London's water supply, supported telecom network planning across hundreds of cities, and developed digital healthcare platforms serving tens of millions of people across Africa. From intelligent command centers to AI-powered infrastructure platforms, we help organizations sense, analyze and act in complex environments.

Our people are ambitious, curious and relentlessly practical. Our teams work alongside clients in the field, solving hard problems and deploying solutions that last. With colleagues across Africa, Europe, the UK and the US, we operate across the full stack - from research and engineering to deployment and capability building.

Our mission is simple: to harness AI to solve humanity's most pressing challenges.

About the role

We are seeking an experienced Data Scientist to join our growing data science team. As a key contributor, the Data Scientist will be responsible for using their advanced data analysis and machine learning skills to solve complex business problems and drive data-driven decision-making within our organisation and those of our clients. The ideal candidate will have a strong background in statistics, machine learning and data analysis, along with a proven track record of delivering impactful, scalable solutions - with demonstrated experience getting data science solutions into production. Key responsibilities are:

  • Conduct independent (and collaborative) research and development of data science and machine learning models; develop cutting-edge data science and machine learning models that drive business value, leveraging internal and external data sources.
  • You are skilled in, and continue to improve upon your knowledge of decision science, communicating data, domain modelling, predictive modelling, advanced analytics, MLOps, Research and AI Ethics with the willingness to up skill others in these competencies.
  • Collaborate with cross-functional teams: work closely with cross-functional teams to apply data science and machine learning models to business problems, ensuring that models are integrated into scalable products and services.
  • Communicate results and impact: communicate results and impact to stakeholders, including technical and non-technical audiences.
  • Perform cutting edge research in Physical AI, at the intersection of engineering models and AI.
  • Mentor junior data scientists: mentor junior data scientists, fostering a culture of continuous improvement and innovation.
Requirements - Essential
  • 3+ years of applied data science experience in water, wastewater, utilities, or smart infrastructure environments.
  • Demonstrated experience working with operational telemetry data (SCADA, AMI, IoT, sensor systems).
  • Strong expertise in time-series modeling, anomaly detection, and forecasting for infrastructure systems.
  • Experience applying geospatial analytics and/or graph/network modeling in real-world systems.
  • Proven track record of deploying ML solutions into production, including data pipelines, MLOps, and model monitoring.
  • Ability to translate advanced analytics into actionable insights for engineering and operations teams.
  • Strong communication skills and experience working with public-sector or regulated environments.
  • M.Sc. (Master's degree) in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field required; PhD preferred.
Location

This role is not a remote position. We would require our Data Scientist to be able to travel to client sites in Baltimore 3-4 days a week minimum.

Personal Attributes
  • Client Centricity & Integrity: We let Our Clients Run the Company, Surf Like Yvon Chouinard to stay true to our values, and Play the Long Game with integrity.
  • Collaboration and Inclusion: We live by Each One, Teach Ten and ensure Everybody is Welcome.
  • Operational Excellence and Simplicity: We K.I.S.S. by keeping things simple while always striving to Raise the Bar.
  • Action, Ownership, and Execution: We Decide, Get Stuff Done, and Do Hard Things with accountability.
  • Growth, Innovation, and Resilience: We Choose Growth, Pioneer boldly, and remember There is No Failure.

Due to the considerable amount of virtual work and interaction with colleagues and customers in different physical locations internationally, it is essential that the successful applicant has the drive and ethics to succeed in working in small teams physically but in larger efforts virtually. Self-drive to communicate constantly using web collaboration and video conferencing is essential.