Geospatial and Machine Learning Engineer at Strathmore University

Basic job summary:

The GIS and Machine Learning Engineer will play a pivotal role in advancing projects that combine Geographic Information Systems (GIS) and Machine Learning (ML) to support innovation in energy access, renewable energy systems, and grid management.

Duties & Responsibilities:
GIS Integration:

• Integrate geospatial systems with existing applications to provide real-time mapping and location-based data.
• Collaborate with teams to ensure data is usable and optimized.

Geospatial Analysis:

• Conduct analysis to derive location-based insights for various projects.
• Develop and execute data collection strategies.
• Create and present maps, dashboards, and visualizations for different audiences.

Machine Learning (ML) for Data Retrieval:

• Design and build machine learning models to automate the extraction of data from unstructured sources.
• Construct and maintain structured datasets for analysis.
• Interpret extracted data to find key specifications and insights

Machine Learning (ML) for Pattern Detection:

• Develop and deploy machine learning tools to automate the detection of specific patterns, such as identifying anomalies or fraudulent activities.

GIS Integration:

• Integrate geospatial systems with existing applications to provide real-time mapping and location-based data.
• Collaborate with teams to ensure data is usable and optimized.

Geospatial Analysis:

• Conduct analysis to derive location-based insights for various projects.
• Develop and execute data collection strategies.
• Create and present maps, dashboards, and visualizations for different audiences.

Machine Learning (ML) for Data Retrieval:

• Design and build machine learning models to automate the extraction of data from unstructured sources.
• Construct and maintain structured datasets for analysis.
• Interpret extracted data to find key specifications and insights.

Machine Learning (ML) for Pattern Detection:

• Develop and deploy machine learning tools to automate the detection of specific patterns, such as identifying anomalies or fraudulent activities.
• Work with stakeholders to align models with project goals.

General Duties:

• Maintain detailed project records, including the use of Gantt charts, to track progress and ensure transparency and accountability.
• Communicate progress metrics and findings through detailed reports, visualizations, and presentations to project managers and stakeholders.
• Stay informed about current best practices and emerging technologies in GIS, data collection, and machine learning to improve efficiency and innovation.
• Work with stakeholders to align models with project goals

Minimum Academic Qualifications:

• Bachelor’s degree in Engineering, Geoinformatics, Computer Science, Statistics, or a related field.

Experience:

• 5+ years of proven experience in GIS analysis and machine learning.
• Strong understanding of GIS principles and ML model development.
• Experience with GIS software (e.g., ArcGIS, QGIS) and ML frameworks (e.g., TensorFlow, PyTorch).
• Proficiency in programming languages like Python and R, and databases like SQL.
• Experience in the energy sector, particularly on electricity-related projects, and familiarity with renewable energy technologies and trends are an advantage.

Competencies and Attributes

• Project Management Skills: Proficiency in project planning, scheduling, risk management, and monitoring and evaluation (M&E).
• Communication Skills: Strong writing skills for developing reports and the ability to communicate complex information clearly to diverse audiences.
• Stakeholder Management: Ability to effectively engage and maintain relationships with diverse stakeholders, including technical and non-technical teams.
• Analytical Skills: Strong analytical, problem-solving, and critical thinking skills to interpret complex data from GIS and ML outputs and produce insightful reports.
• Research Skills: Experience in designing and conducting research, including both quantitative and qualitative methodologies.
• Familiarity with renewable energy technologies and trends.