WILSON E.
Mid-Level Data Scientist
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Wilson E., based in Copenhagen, Denmark, is an accomplished Applied Data Scientist and Data Systems Engineer with an advanced degree in Engineering Artificial Intelligence from Carnegie Mellon University. Specializing in spatio-temporal data systems and geospatial analytics, Wilson has engineered production-grade data pipelines crucial for humanitarian and policy decision-making. Currently with UNHCR, Wilson is responsible for a geospatial intelligence dashboard facilitating extensive spatial and temporal analysis of displacement data. His expertise includes designing secure, low-latency API architectures using FastAPI and Docker on Azure platforms, ensuring seamless integration with multi-source datasets. Previously, at Carnegie Mellon University and in projects funded by the World Bank and UNDP, Wilson demonstrated leadership in backend data processing and machine-learning solutions for environmental and infrastructure initiatives. His technical arsenal includes proficiency in Python, Spark, and Azure Databricks, underscoring his commitment to leveraging data science for impactful decision-making.
Experience
Data Scientist
UNHCR – The UN Refugee Agency · 2025 — PresentConstructed a geospatial intelligence dashboard that facilitates interactive spatial and temporal analysis of numerous displacement-related risk observations. Created and implemented a secure, cloud-native API layer using FastAPI and Docker on Azure Container Apps to provide low-latency access to curated climate, agriculture, conflict, and demographic indicators. Validated and integrated multi-source datasets from Azure Data Lake, ensuring quality and consistency while aligning with UNHCR operational workflows. Deployed automated batch inference pipelines on Azure Databricks to support predictive and monitoring analytics aligned with crisis-monitoring cycles. Established reproducible deployment workflows through Azure DevOps CI/CD.Research Associate (Software & Data Systems)
Carnegie Mellon University · 2024 — 2025Engineered comprehensive data and software systems for low-power environmental monitoring, covering aspects from embedded data capture to analytics workflows. Created data calibration and validation pipelines enhancing sensor accuracy and reliability. Assisted in the preparation of technical documentation, research outputs, and stakeholder reporting.Data Scientist / Software Engineer (World Bank–Funded Project)
Geo-Information Communication Ltd · 2022 — 2024Directed backend data processing and geospatial analysis for a national energy access platform, managing the cleaning and validation of over 1.55 million records utilizing Python, SQL, and PostGIS. Generated analytical indicators and GIS-driven dashboards to facilitate strategic planning and infrastructure development. Coordinated with domain experts to verify assumptions and validate the relevance of analytical outputs.Machine Learning Engineer (UNDP-funded Project)
Triaxis Geomatics · 2021 — 2022Analyzed over 400GB of satellite imagery datasets focused on land-use and environmental monitoring. Developed time-series models that achieved 98.4% accuracy, aiding in evidence-based environmental management decisions.Machine Learning Engineer
Kyambogo University GIS Lab · 2022 — 2022Created machine-learning pipelines designed for satellite imagery analysis, obtaining a 96.2% F1 score in the extraction of building footprints.
Skills & Expertise
Education
- MSc, Engineering Artificial IntelligenceCarnegie Mellon University · — — 2025
- BSc, Computer EngineeringMakerere University · — — 2022