Data Analyst / Data Scientist working across ecological, financial, and aerospace datasets.

I build clean, reproducible analysis pipelines end-to-end — data → features → evaluation → packaging — with an emphasis on clarity, structure, and decision-ready outputs.

• Turn messy real-world data into clean, structured datasets
• Build reproducible research workflows and modeling pipelines
• Design leakage-free experiment structures and evaluation splits
• Communicate results clearly through plots, maps, and short technical writeups

• Environmental + ecological data analysis
• Geospatial and spatiotemporal reasoning
• Data validation + quality checks
• Clear, defensible methodology
• Communicating results through accessible visuals and short technical narratives

📈 Quanta Multi‑Signal Equity Strategy (Flagship) 50-signal systematic equity strategy with disciplined TRAIN → VALIDATION → LOCKED → HOLDOUT workflow.

Signals: experiment rigor, leakage controls, reproducibility.

🌱 Environmental + Biodata Analyses

• Environmental data
• Data visualizations

Signals: geospatial reasoning, environmental modeling, communication.

✈️ Aerospace + Security Data Analyses Notebooks transforming raw aerospace/security datasets into interpretable insights.

Signals: data cleaning, feature engineering, visual clarity.

Python SQL R pandas scikit-learn Jupyter matplotlib/plotly geospatial tooling

Reproducible by default: clean repo structure, documented assumptions, deterministic evaluation splits.

I bias toward clarity, metrics, and methodological discipline over flashy modeling.

LinkedIn: https://www.linkedin.com/in/kate-mason99