1

Machine Learning Geospatial Jobs in South Carolina

Experience utilizing geospatial analytics and external market data sources. * Experience with AWS cloud services and modern AI platforms. Essential Skills: * Data Science & Machine Learning: Solid ...

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 ...

Showing results 21-23

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 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 popular job titles related to Machine Learning Geospatial jobs in South Carolina? For Machine Learning Geospatial jobs in South Carolina, the most frequently searched job titles are:
What job categories do people searching Machine Learning Geospatial jobs in South Carolina look for? The top searched job categories for Machine Learning Geospatial jobs in South Carolina are:
What cities in South Carolina are hiring for Machine Learning Geospatial jobs? Cities in South Carolina with the most Machine Learning Geospatial job openings:

Data Scientist

Maymont Homes

Charleston, SC • Remote

Full-time

Retirement, PTO

Posted 22 days ago


Job description

Location

Charleston - 997 Morrison Drive, Suite 402

Business

Our Growth, Your Opportunity

At Maymont Homes, our success starts with people, our residents and our team. We are transforming the single-family rental experience through innovation, quality, and genuine care. With more than 20,000 homes across 47+ markets, 25+ build-to-rent communities, and continued expansion on the horizon, we are more than a leader in the industry-we are a company that puts people and communities at the heart of everything we do.

As part of Brookfield, Maymont Homes is growing quickly and making a lasting impact. We are also proud to be Certified by Great Place to Work, a recognition based entirely on feedback from our employees. This honor reflects the culture of trust, collaboration, and belonging that makes Maymont a place where people thrive.

Join a purpose-driven team where your work creates opportunity, sparks innovation, and helps families across the country feel truly at home.

Job Description

**This position is onsite at 997 Morrison Dr, Charleston SC**

Primary Responsibilities: The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to solve complex business challenges and enable data-driven decision-making across the organization. This role develops, validates, and deploys predictive and statistical models using Python, while also performing hands-on data analysis, data extraction, and ad hoc reporting using Python, SQL, and Excel.

Working closely with cross-functional business partners, the Data Scientist translates business questions into analytical solutions, delivering actionable insights that support strategic initiatives and operational decision-making. The role is responsible for owning the end-to-end modeling lifecycle, including problem definition, data preparation, model development, validation, performance evaluation, and communication of results to both technical and non-technical audiences.

The ideal candidate combines strong expertise in data science, machine learning, and statistical analysis with practical proficiency in Python, SQL, and Excel. They are intellectually curious, analytical, and comfortable working with complex datasets to uncover meaningful insights. Success in this role requires the ability to quickly develop domain expertise in the housing industry, collaborate effectively with business stakeholders, and translate technical findings into clear, impactful recommendations that drive business value.

Skills & Competencies:

Qualifications:

  • Bachelor's degree in Data Science, Statistics, Economics, Finance, Applied Mathematics, Computer Science, Engineering, or a related quantitative field.

  • 3+ years of experience in data science, analytics, or applied quantitative work.

  • Strong problem-solving skills and attention to detail.

  • Strong Python, SQL, and Excel skills, with the ability to handle ad hoc data requests from business partners.

  • Excellent communication, collaboration, and presentation skills with both technical and business audiences.

  • Familiarity with Git, Agile development methodologies, and collaborative software development practices.

Preferred Qualifications:

  • Experience within real estate, private equity, investment management, asset management, or financial services.

  • Experience building and deploying predictive pricing, forecasting, or optimization models in production.

  • Experience utilizing geospatial analytics and external market data sources.

  • Experience with AWS cloud services and modern AI platforms.

Essential Skills:

  • Data Science & Machine Learning: Solid working knowledge of statistical modeling, predictive analytics, regression, and core machine learning methods, with hands-on experience building models.

  • Problem Solving: Ability to take a defined business problem, develop an analytical approach, and translate findings into clear, usable recommendations for business partners.

  • Excel & Ad Hoc Analysis: Advanced Excel skills, including the ability to quickly turn around ad hoc data requests, build clear analyses, and summarize results for business partners such as Asset Management and Operations.

  • Programming: Strong Python and SQL skills for building models and analyzing data, with hands-on experience using common libraries (e.g., pandas, scikit-learn).

  • Artificial Intelligence: Baseline experience working with AI tools, including an understanding of prompts and prompt engineering to improve analytical efficiency.

  • Model Deployment: Exposure to how models are deployed to production and monitored over time, with willingness to develop these skills alongside team members.

  • Collaboration: Ability to work effectively across data science, engineering, and business teams, building strong partnerships and contributing to shared goals.

  • Communication: Ability to clearly communicate complex analytical concepts to technical and non-technical audiences.

Essential Job Functions:

Typical Day Activities:

  • Partner with business teams, including Asset Management and Operations, to handle ad hoc data requests and support day-to-day operational and portfolio questions.

  • Build predictive models in Python to address defined business problems, such as pricing, occupancy, or operational performance, in collaboration with senior team members.

  • Help deploy models into production and monitor their performance, learning production best practices with support from senior team members and Data Engineering.

  • Summarize findings into clear, concise takeaways for business partners.

  • Collaborate with Data Engineering to ensure scalable, reliable, and trusted analytical datasets.

Key Metrics & Responsibilities:

  • Decision Support: Provide timely, accurate analysis and ad hoc data support that helps business partners make better decisions.

  • Model Building: Build and maintain models in Python that reliably address the business problems they are assigned to solve.

  • Quality of Analysis: Deliver accurate, well-organized analyses that business partners can trust and use.

  • Growth & Learning: Steadily expand technical skills and business knowledge, including new tools and modeling techniques and the housing industry, over time.

  • Data Quality & Analytical Standards: Ensure analytical rigor, statistical integrity, reproducibility, and documentation across all models and analyses.

Why work for Maymont Homes?

Our Mission - "We Positively Impact the Lives in the Communities We Serve." Every role contributes to this purpose, helping families find a place to call home while making a difference in the communities we support.

Certified Great Place to Work - Our people make us who we are. This certification celebrates the values and culture that fuel collaboration, innovation, and care.

Outstanding Benefits - Backed by Brookfield, our benefits include a 5% 401(k) match, wellness credits that reduce healthcare costs, and up to 160 hours of PTO annually for full-time employees.

Career Growth - With continued expansion planned for Maymont, you'll find meaningful opportunities to grow your skills, advance your career, and make an impact.

Strong Foundation - As part of Brookfield Asset Management, one of the world's largest real estate asset managers, we have the stability, resources, and vision to keep growing.

Equal Opportunity Employer: Minorities/Religion/Sex/Protected Veterans/Disability/Sexual Orientation/Gender Identity/Marital Status/Pregnancy/Age/National Origin/Genetic Information. #MYMT