Presentation
My services cover the complete data science workflow, including data cleaning, exploratory data analysis, feature engineering, model development, validation, and deployment. I work extensively with tools such as Python, R, SQL, and Excel to analyze complex datasets and build predictive models using techniques like regression analysis, classification algorithms, time-series forecasting, and advanced machine learning methods including Random Forest, Gradient Boosting, and XGBoost.
I also have expertise in statistical and econometric modeling, including hypothesis testing, causal analysis, panel data models, and forecasting. For financial and risk-related applications, I can develop credit risk models, probability of default (PD) models, behavioral scorecards, and other predictive risk analytics frameworks.
In addition, I design clear and interactive dashboards and visualizations to communicate insights effectively to stakeholders and leadership teams. My goal is to deliver reliable, well-documented, and scalable analytical solutions that help clients understand their data and make informed decisions.
Whether you need help analyzing a dataset, developing a machine learning model, forecasting trends, or building a data-driven decision system, I can provide structured and high-quality solutions tailored to your specific needs.
Offered services
Background
• Built time‑series forecasting models for customer payments and spending behavior, reducing forecast MAPE by ~20%.
• Designed portfolio segmentation and clustering models to support marketing and campaign targeting strategies.
• Developed propensity model to predict customer purchase behavior improving campaign conversion rates by ~8%
business problems.
• Delivered predictive analytics solutions that improved operational decision‑making.
performance.
• Implemented NLP techniques to analyze customer feedback and sentiment for product improvement.
• Built machine learning models such as Random Forest, Support Vector Machine (SVM), and XGBoost to improve predictive accuracy.
• Presented analytical findings and recommendations to senior management, influencing strategic decision- making and driving organizational improvement.
• Improved forecasting accuracy through the implementation of advanced statistical techniques, enhancing business planning and resource allocation.
• Automated data extraction and reporting workflows to improve operational efficiency.


