Data Scientist with hands‑on experience in machine learning, statistical modeling, and data visualization. Skilled in Python, SQL, Tableau, Power BI, and Azure, with a track record of building predictive models, ETL pipe…
Data Scientist with hands‑on experience in machine learning, statistical modeling, and data visualization. Skilled in Python, SQL, Tableau, Power BI, and Azure, with a track record of building predictive models, ETL pipelines, and interactive dashboards across domains such as real estate, finance, healthcare, and environment. Experienced in time‑series forecasting, clustering, and A/B testing, with a strong ability to translate complex datasets into actionable insights. Currently completing a B.Sc. in Software Engineering (expected July 2025) and certified in Applied Data Science by WorldQuant University.
Built predictive models (linear regression, logistic regression, decision trees, random forest, gradient boosting) to analyze housing, earthquake damage, and bankruptcy datasets.
Developed time-series forecasting models (ARMA, GARCH) to predict air quality and financial volatility, improving accuracy through hyperparameter tuning.
Designed ETL pipelines in Python and SQL to clean, transform, and integrate data from CSV, SQLite, and MongoDB sources.
Created interactive dashboards in Tableau, Power BI, and Plotly Dash to visualize customer segmentation, housing trends, and project outcomes.
Applied statistical testing (chi-square, correlation analysis) to evaluate A/B testing results and uncover significant relationships in datasets.
Leveraged cloud technologies (Azure Data Lake, Databricks, PySpark) to process large datasets and optimize workflows.
Communicated insights through professional reports and visualizations, supporting decision-making in diverse domains (real estate, healthcare, finance, environment).
Built predictive models (linear regression, logistic regression, decision trees, random forest, gradient boosting) to analyze housing, earthquake damage, and bankruptcy datasets., Developed time-series forecasting models (ARMA, GARCH) to predict air quality and financial volatility, improving accuracy through hyperparameter tuning., Designed ETL pipelines in Python and SQL to clean, transform, and integrate data from CSV, SQLite, and MongoDB sources., Created interactive dashboards in Tableau, Power BI, and Plotly Dash to visualize customer segmentation, housing trends, and project outcomes., Applied statistical testing (chi-square, correlation analysis) to evaluate A/B testing results and uncover significant relationships in datasets., Leveraged cloud technologies (Azure Data Lake, Databricks, PySpark) to process large datasets and optimize workflows., Communicated insights through professional reports and visualizations, supporting decision-making in diverse domains (real estate, healthcare, finance, environment).
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