Soil Science Researcher transitioning to Data Science. Combining environmental expertise with modern analytics, ML, and AI-driven workflows.
Currently a Soil Science Researcher at HUN-REN Centre for Agricultural Research, analyzing organic matter content using statistical methods in Python and R. Experienced in creating comprehensive reports and presentations, and handling large datasets with precision.
Holding a PhD from MATE in Agroecology, my doctoral research investigated how soil regeneration methods impact microbial communities and carbon forms — building deep expertise in regenerative agriculture, soil biology, and inoculant development.
With a background in plant biology, genetics, and biotechnology — including hands-on tissue culture and enzyme kinetics experience — I bring a rigorous scientific mindset to every challenge. Now seeking Junior Data Scientist roles that combine environmental expertise with ML, SQL, and modern analytics. Open to Hungary-based and remote opportunities.
Exploratory data analysis on soil properties — pH, organic matter, nutrients. Statistical summaries and publication-quality visualizations.
Regression models predicting SOC from soil properties. Linear Regression, Random Forest, and XGBoost with SHAP interpretability.
End-to-end ML pipeline: data ingestion, model training with MLflow tracking, Docker containerization, and FastAPI deployment.
10 progressive projects from EDA to production ML — bridging soil science with data science.
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