Open to opportunities

abdelmahdi lahrach

@abdelmahdilahrach

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Environmental geomatics GIS officer applying GIS, remote sensing, and ML to water-resource challenges.

Morocco

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What I'm looking for

I’m looking for international GIS, remote-sensing, and geospatial-data roles where I can combine GIS/EO and ML to support water-resources and environmental projects, contributing to reliable field-to-data workflows in remote, hybrid, or on-site teams.

I’m an Environmental geomatics specialist (MSc) applying GIS, remote sensing, and machine learning to water-resources and environmental challenges. I built machine-learning and deep-learning models to forecast groundwater levels from multi-source climatic and hydrological data, and I’ve worked hands-on across land-use change detection, watershed characterization, and operational GIS.

In my current role as a GIS Officer for an FTTH project, I operate and maintain GIS for the deployment and management of a fiber-optic network and coordinate field teams with a focus on data-quality and reliability. Earlier, as a research intern, I developed end-to-end forecasting workflows (data integration, cleaning, feature engineering, model training, and evaluation), and through my internships I delivered regional cartographic outputs and built a GIS solution to support land-subdivision planning and management.

Experience

Work history, roles, and key accomplishments

Current

GIS Officer - FTTH Project

Current

HUSB

Jan 2025 - Present (1 year 6 months)

Operate and maintain GIS for the deployment and management of a FTTH fibre-optic network. Coordinate and supervise field teams to enforce data-quality and reliability standards across the network.

Data Quality Management Network Data Reliability

Research Intern

GeanTech / Mohammed VI Polytechnic University (UM6P)

Mar 2024 - Jul 2024 (4 months)

Developed machine-learning and deep-learning models to forecast groundwater piezometric levels from multi-source climatic, hydrological, and environmental data. Built an end-to-end workflow including data integration/cleaning, feature engineering, model training, and evaluation.

Machine Learning Deep Learning Data Integration Data Cleaning Feature Engineering Model Training Model Evaluation