Analytical & Modelling
- Statistical modelling: GLM, GAM, mixed-effects, hierarchical models
- Spatial modelling: species distribution models, spatiotemporal analysis, spatial autocorrelation, spatial forecasting
- Machine learning: ensemble methods, gradient boosting, neural nets, MaxEnt
- Bayesian inference: hierarchical & spatiotemporal models, detection–abundance separation
- Forecasting & simulation: population viability, simulation-based forecasting, scenario testing
- Model evaluation: cross-validation (incl. spatial CV), calibration & error metrics (RMSE/MAE, PPCs)
- Probability modelling: hypergeometric, Monte Carlo simulation
Technical Stack
- Languages: R, Python, SQL, JAGS
- Python data stack: pandas, NumPy, scikit-learn; OOP (composition, inheritance/polymorphism); CLI apps (argparse/click); regex; logging & error handling
- Data acquisition & I/O: CSV/Excel/JSON, PDF parsing basics, HTTP APIs (requests), web scraping (BeautifulSoup/lxml)
- Databases & storage: SQLite, Microsoft Access; SQL querying (SELECT/JOIN/AGGREGATE, CTEs/window functions – foundational)
- Reproducible workflows: version control (Git/GitHub – branching/PRs), environments (renv, venv/conda), parallel processing
- Visualisation: R (ggplot2/base), Python (matplotlib, Plotly)
- Geospatial/remote sensing: raster/vector processing, spatial joins, satellite-derived metrics (terra/sf or equivalents)
- Testing & quality: unit tests with pytest (foundational), assertions, data validation checks
- Packaging & docs (foundational): module structure, docstrings/type hints, README/usage examples
- Web & apps: GitHub Pages (this site), Shiny apps (R)
Applied Expertise
- Pattern detection in complex, high-dimensional datasets
- Forecasting to predict trends, risks, and opportunities
- Tailored statistical analysis aligned to data & objectives
- Decision-support tools: interactive apps/dashboards for stakeholders
- Workflow optimisation: high-performance/parallel pipelines, memory-efficient geoprocessing
- Translation to action: clear, actionable recommendations for managers & policy
- Data-driven frameworks for strategic decision-making
Communication
- Writing: lead & co-author on peer-reviewed papers; clear technical documentation
- Speaking: conference talks, workshops, stakeholder briefings; tailored to technical & non-technical audiences