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csim063/README.md

Hi there, I am Craig Simpkins PhD 👋

Visitors

Data scientist and ecologist based in Auckland, New Zealand. I work across research, government, consulting, and corporate, where I excel at solving complex data challenges, especially in environment and ecology, using advanced data analysis, machine learning, spatial statistics, and generative AI.

I also run Emergent Data Analytics, providing ecological modelling, geospatial analysis, and data science services for anyone who can benefit from them.


🛠️Key Skills

Machine Learning & Predictive Modelling

  • End-to-end model development, from feature engineering to deployment and clear interpretation of results.

Time-Series & Spatial Analysis

  • Specialising in complex, location-based and time-dependent data to uncover deeper, contextual insights that standard analyses miss.

Cloud Data Architecture & Pipelines

  • Designing and implementing efficient, scalable, and reproducible data analysis workflows.

Statistical Consulting & Research

  • Providing expert experimental design, rigorous statistical validation, and clear technical reporting for major initiatives.

🚀 Selected Projects

Project Description Technologies
Te Kawau Tūmārō o Toi Co-authored the baseline assessment for a major multi-species eradication programme, synthesizing large-scale vegetation and bird community data. R, renv, Quarto, QGIS
Time-series modelling analysis Developed and benchmarked a suite of time-series models (from ARIMA to ML ensembles) to predict ecological outcomes using a messy public dataset, providing clear guidance on model selection under differing scenarios. R, Python, Scikit-learn, git
Spatial network design Designed a spatially-optimised sampling network for vegetation monitoring, using spatial statistics to maximise data quality while minimising operational costs. R, SQL, QGIS, git
Conservation prioritisation tool Developed a customised structured decision support tool for a local council to identify and rank areas with the highest priority for conservation investment. Python, SQL, ArcGIS, git

💻 Core Tech Stack

My Skills


📫 Get in Touch

I'm always open to discussing new projects or consulting & contracting opportunities. If you're facing a data challenge that requires deep analytical expertise, let's connect.

Pinned Loading

  1. ropensci/NLMR ropensci/NLMR Public

    📦 R package to simulate neutral landscape models 🏔

    R 66 17

  2. landscapetools landscapetools Public

    Forked from ropensci/landscapetools

    📦 R package for some of the less-glamorous tasks involved in landscape analysis 🌏

    R

  3. r-spatialecology/spectre r-spatialecology/spectre Public

    C++ 8 1

  4. biosampleR biosampleR Public

    Provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation o…

    R

  5. NRT_Forest_Model_jl NRT_Forest_Model_jl Public

    Re-implementation of a spatially explicit (grid-based) model of NZ forest dynamics - see Morales et al. 2017 (doi: 10.1016/j.ecolmodel.2017.04.007) and Brock et al. 2020 (doi: 10.1111/1365-2745.133…

    Julia 1 1

  6. LandSciTech/caribouMetrics LandSciTech/caribouMetrics Public

    Models and metrics of boreal caribou demography and disturbance effects

    R 4 3