Data Scientist | Health & Business Insights
MSc Data Science for Food and Health student at Wageningen University. Passionate about leveraging data to uncover insights in public health, sustainability, and business performance.
Explore my portfolio and discover how data science can illuminate complex challenges in health, sustainability, and business.
My motivation stems from a desire to make a positive impact. I believe data science has the power to uncover hidden patterns that lead to better health outcomes (like predicting CKD or PCOS), drive sustainable practices (e.g., forecasting food emissions), optimize business strategies (through CRM analysis and retail promotion insights), and enhance customer experiences (via chatbot performance analysis). By leveraging data, I aim to contribute to solving complex problems and improving quality of life.
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Chat Flow Content & Performance Analyst
Foot Locker EMEA
Apr 2024 - Sep 2024
Developed and maintained a Power BI dashboard to monitor chatbot performance across EMEA markets, focusing on KPIs like deflection rate and escalations.
Analysed chatbot interaction data to identify trends, diagnose performance issues, and pinpoint areas for improvement in customer experience.
Managed integration aspects and performance optimization for conversational agents.
CRM Marketer
Wageningen University & Research
Oct 2022 - Apr 2024
Led CRM integration projects (e.g., unifying Wageningen Academy & Professional Education systems) to enhance customer data accuracy and enable cross-selling.
Contributed to the development of the CRM strategy, focusing on data structure optimization and enabling data-driven marketing initiatives.
Developed Python scripts for automating data tasks and wrote SQL queries for analysis.
Optimized codebase for system integration and adhered to clean coding standards.
Master of Science - Data Science for Food and Health
Wageningen University & Research
Sep 2024 - present
Bachelor of Science - Business and Consumer Sciences
Wageningen University & Research
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Explore my areas of expertise and discover how I can elevate your projects.
Proficient in Python (Pandas, Scikit-learn), and statistical analysis for extracting insights from complex datasets.
Experience in classification (Random Forest, Logistic Regression), predictive modeling, and feature engineering for applications in healthcare and business.
Skilled in analyzing temporal data, forecasting (ARIMA, VAR), and identifying trends, applied to areas like sustainability.
Familiarity with processing large datasets using tools like Apache Spark for retail analytics.
Expertise in developing interactive dashboards using Power BI to monitor KPIs and communicate performance insights effectively.
Experience building Knowledge Graphs using RDFLib and mapping data to medical ontologies (SNOMED-CT, LOINC) for healthcare data integration.
Experience in CRM system integration, data strategy development, and optimizing CRM for data-driven marketing.
Building robust data pipelines, handling missing data, outlier detection, and ensuring data quality for reliable analysis.
Experienced in leading technical projects, collaborating with stakeholders, and ensuring alignment with business objectives.
Dive into case studies covering healthcare predictions (CKD, PCOS), sustainability forecasting (food emissions), retail analytics (SPAR promotions), CRM strategy, chatbot performance analysis, and knowledge graph construction.
Applied Random Forest and Logistic Regression to predict Polycystic Ovary Syndrome (PCOS) from clinical data, achieving high accuracy and identifying key predictive factors like Follicle Number and Cycle regularity. This project highlights AI's potential in enhancing early diagnosis for better patient outcomes.
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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Fabian Schreuder