I blend machine learning, data engineering, and strategic analysis to solve complex problems in healthcare, sustainability, and business.
A data scientist with a passion for transforming complex data into actionable insights and robust data products. My expertise spans predictive modeling, big data engineering, and developing data-driven strategies, with a strong focus on ethical and responsible AI.
I approach data science projects with a comprehensive methodology that combines statistical rigor, computational efficiency, and strategic foresight. My goal is to build solutions that are not only technically sound but also drive tangible value and positive outcomes.
Specialization in Health & Sustainability Analytics
Wageningen University & Research • 2024 - present
Focus on Data Analytics & Consumer Behavior
Wageningen University & Research • 2022
Built classification (CKD, PCOS) & survival models (cancer recurrence) using ML & advanced statistics.
Engineered a Spark-based system to analyze 70M+ transactions for cannibalization & basket size insights.
Developed ARIMA & VAR models to forecast global food emissions from complex, multi-source data.
Data Ethics & Responsible AI
Knowledge Graphs & Semantics
Big Data Systems
Data-Driven Strategy
My career and educational path is defined by leading and executing impactful data projects at the intersection of industry and academia.
Foot Locker EMEA
Developed and maintained a Power BI dashboard to monitor chatbot performance across EMEA markets. Analyzed interaction data to identify trends, diagnose issues, and pinpoint areas for improvement in customer experience and deflection rates.
Wageningen University & Research
Authored the strategic CRM plan to unify data systems and enable data-driven marketing. Led the subsequent CRM integration project, managing stakeholders, budget, and technical execution to create a unified customer view and enhance cross-selling capabilities.
Focus on Healthcare, Sustainability, and Big Data Analytics
Wageningen University & Research
Executed a diverse portfolio of data science projects, including building ML models for disease prediction (CKD, PCOS), developing survival models for cancer research (CRC), forecasting sustainability metrics with time series analysis (ARIMA, VAR), and constructing a healthcare knowledge graph using RDF and medical ontologies.
Focus on Data Analytics & Consumer Behavior
Wageningen University & Research
Developed a strong foundation in statistical analysis, research methodology, and business intelligence, culminating in a big data project analyzing retail promotions with Apache Spark.
My technical toolbox is built on a foundation of modern data science, engineering, and analytics technologies, allowing me to build robust, end-to-end data solutions.
Dive into a portfolio of case studies showcasing my work in healthcare predictions, big data retail analytics, sustainability forecasting, CRM strategy, and ethical AI.
Building and evaluating machine learning models to predict Polycystic Ovary Syndrome (PCOS) using clinical and biochemical features, achieving high accuracy for early diagnosis.
The multi-billion dollar Direct-to-Consumer (DTC) genetic testing industry is an ethical minefield. This post breaks down the key risks—from data privacy to flawed health reports—and proposes a framework for responsible innovation using data justice and co-design principles.
A case study on using Latent Profile Analysis and Cox Proportional Hazards modeling to identify pre-diagnosis dietary patterns associated with colorectal cancer recurrence. We navigated complex, high-dimensional health data to deliver clinically relevant insights.
A case study on validating a new Food Frequency Questionnaire (FFQ) for dietary fiber intake. This post details the use of 24-hour dietary recalls as a reference method and the application of linear regression calibration to correct for measurement error, enhancing data accuracy for epidemiological research.
Analyzed multi-source data on global food production, population, and emissions using time series techniques. Developed and compared forecasting models (ARIMA, VAR) to predict future trends.
Leveraging machine learning on clinical data to build a robust model for early Chronic Kidney Disease (CKD) detection, improving patient outcomes and potentially reducing healthcare costs.
Leveraging Python, RDFLib, and standard medical ontologies (SNOMED-CT, LOINC) to transform tabular Chronic Kidney Disease data into a structured Knowledge Graph, highlighting data integration and problem-solving.
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