Zijie Wang - GIS analyst - Previsico Ltd

Summary

PhD-qualified flood modelling engineer and GIS analyst with hands-on industry experience spanning the full pipeline from core hydraulic simulation to large-scale cloud infrastructure and client delivery. Combines doctoral-level scientific rigour with practical engineering skills in C++ model development, Python automation, and geospatial data engineering. Delivered production-grade geospatial products at national scale, optimising cloud resource utilisation across 100+ instances while maintaining cost discipline. Brings a rare combination of academic depth, engineering pragmatism, and project ownership that spans research-grade modelling through to commercial product delivery.

Overview

4

years of professional experience

11

years of post-secondary education

Work history

GIS analyst

Previsico Ltd

Loughborough, Leicestershire

05.2022 - Current

Probabilistic Flood Library

Designed, produced, and delivered the UK nationwide Flood Library product (40k tiles, 200+ rainfall scenarios, 5 m and 25 m resolution) by building and managing a cloud-based production pipeline using Python, GCP, Terraform, and AWS S3 — scaling to 100+ GCP instances, optimising resource utilisation to deliver the entire pipeline on a £10,000 GCP credit budget. The product now underpins the company's commercial forecast service.

Core Flood Model Development

Improved the stability rate of the core C++ flood model from 95% to 99%, significantly reducing costly simulation reruns at scale. Collaborated cross-functionally to maintain and enhance the model for broader real-world flood modelling applications.

Compound Flood Modelling & Client Delivery

Built a prototype compound flood model integrating surface water and river simulations for a South Africa client engagement, leading report delivery and coordinating technical contributions across teams.

Operational Support & Knowledge Transfer

a. Delivered Flood Risk Assessments for clients, encompassing end-to-end data sourcing and processing to modelling-ready quality standards in support of the sales team.

b. Led the Apache Airflow onboarding initiative, designing and delivering knowledge transfer sessions to upskill the wider team on workflow orchestration applications.

Education

Doctor of Philosophy - Geography and Environment

Loughborough University

Loughborough

09.2021 - 07.2026

Master of Science - Safety Engineering

Tsinghua University

Beijing, China

09.2018 - 06.2021

Bachelor of Science - Nuclear Science and Technology

Harbin Engineering University

Harbin, China

09.2014 - 06.2018

Skills

Office : Microsoft 365, Jira, Confluence

Programming : Python, C, Git

GIS : ArcGIS, QGIS, GDAL

Cloud & Infrastructure : GCP, AWS, Terraform

Tools : Apache Airflow, Claude Code

Data Formats : GeoTIFF, NetCDF, Shapefile, GeoPackage, Parquet

Publication

Wang, Z. (2026). Scaling techniques for extreme rainfall estimation at the local-to-global scale [Doctoral thesis, Loughborough University]. https://doi.org/10.26174/thesis.lboro.31267003.v1

Wang, Z., Wilby, R. L., & Yu, D. (2025). Forecasting global rainfall in a changing climate: A machine learning approach using Köppen-Geiger zones. Earth Systems and Environment. https://doi.org/10.1007/s41748-025-00876-9

Wilby, R. L., Wang, Z., Courty, L. G., Poschlod, B., Arfa, S., Hassanzadeh, E., & Yu, D. (2025). Preliminary assessment and comparison of state-of-the-art methods for forecasts and projections of hourly extremes (Report No. D4.1). ClearClimate Consortium. https://doi.org/10.13140/RG.2.2.23433.15203

Wang, Z., Wilby, R. L., & Yu, D. (2024). Spatial and temporal scaling of extreme rainfall in the United Kingdom. International Journal of Climatology, 44(1), 286–304. https://doi.org/10.1002/joc.8330

Languages

English

Fluent

Chinese (Mandarin)

Native

Timeline