Curriculum Vitae
Highlights
- Astrophysics PhD applying scientific rigor to data work.
- Build end-to-end: pipelines, analysis, and interpretable visuals.
- Communicate clearly: concise notes, links to code, and reproducible results.
- Actively seeking junior / early career data roles.
Skills
Core skills
- Python, SQL, pandas, NumPy
- scikit-learn (ML), statistical modeling
- Data pipelines and wrangling
- Visualization: Plotly, Matplotlib
- Reproducible workflows: Jupyter, Git
Exploring
- PyTorch (CNNs, transfer learning)
- Docker for packaging
- HPC / SLURM environments (from research)
Transferable Competencies from Research
Supercomputing & allocations
- Ran large‑scale simulations at CINES (FR), Fermilab (US), and RES/Barcelona (ES).
- Awarded millions of CPU‑hours via peer‑reviewed calls; delivered within quota and timeline.
- Efficient with batch schedulers (SLURM), job arrays, and parallel workflows.
Open‑source & engineering
- Maintainer for a public community release (AM3 documentation).
- Author of RIPTIDE: vectorised Python, parallel processing, and high‑performance I/O to process and simulate signals.
- Reproducible pipelines: clear configs, versioned data outputs, and CI‑ready scripts.
Modelling & analysis
- Fit models to data using least‑squares optimisation; ran parallel fits across parameter ranges.
- Quantified uncertainty and validated results against independent data; communicated limits and assumptions clearly.