Chen David

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Data Analyst-Fraud detection
Extracted insights using complex SQL queries from
large relational databases. Created dashboards with
Power BI, Tableau, and Excel. Detected data anomalies
and contributed to operational decision-making

Data science Intern
Ruppin Academic Center, in collaboration wit Lev Hasharon Mental
Health Center
- Built a machine learning pipeline to predict patient readmissions within
30 and 90 days using real clinical data.
-Cleaned, processed, and structured and unstructured medical data using
Python.
-Performed statistical analysis, hypothesis testing, and feature engineering
based on demographic and clinical variables to improve model performance.
-Built and optimized machine learning models including Logistic
Regression, Random Forest, and XGBoost.
-Applied advanced NLP techniques (BERT) to analyze admission and
discharge notes written by physicians.
-Authoring an academic article intended for publication

Data Analyst-Fraud detection
Extracted insights using complex SQL queries from
large relational databases. Created dashboards with
Power BI, Tableau, and Excel. Detected data anomalies
and contributed to operational decision-making

Data science Intern
Ruppin Academic Center, in collaboration wit Lev Hasharon Mental
Health Center
- Built a machine learning pipeline to predict patient readmissions within
30 and 90 days using real clinical data.
-Cleaned, processed, and structured and unstructured medical data using
Python.
-Performed statistical analysis, hypothesis testing, and feature engineering
based on demographic and clinical variables to improve model performance.
-Built and optimized machine learning models including Logistic
Regression, Random Forest, and XGBoost.
-Applied advanced NLP techniques (BERT) to analyze admission and
discharge notes written by physicians.
-Authoring an academic article intended for publication

Data science Intern
Ruppin Academic Center, in collaboration wit Lev Hasharon Mental
Health Center
- Built a machine learning pipeline to predict patient readmissions within
30 and 90 days using real clinical data.
-Cleaned, processed, and structured and unstructured medical data using
Python.
-Performed statistical analysis, hypothesis testing, and feature engineering
based on demographic and clinical variables to improve model performance.
-Built and optimized machine learning models including Logistic
Regression, Random Forest, and XGBoost.
-Applied advanced NLP techniques (BERT) to analyze admission and
discharge notes written by physicians.
-Authoring an academic article intended for publication