
AI‑Driven Sales Forecasting (Python, TensorFlow, AWS SageMaker)
Improved retail demand forecast accuracy by 30% using LSTM, GRU, ARIMA, and XGBoost. Deployed production pipeline on AWS SageMaker with automated retraining.
6+ years turning complex data into actionable insights with Python, SQL, Power BI, AWS, and Data Visualization.
Improved retail demand forecast accuracy by 30% using LSTM, GRU, ARIMA, and XGBoost. Deployed production pipeline on AWS SageMaker with automated retraining.
Built an interactive Power BI dashboard analyzing 2 years of sales with KPIs (Gross Profit, Net Margin, Sales Trends). Helped managers optimize inventory and marketing.
Responsive multi‑page dashboard with scatter, histogram, pie and stacked bar charts; enabled real‑time exploration across datasets.
I’m a Data Scientist & Analyst with 6+ years of experience in system engineering, cloud platforms, and data-driven applications, and an M.S. in Data Science from Gannon University. Skilled in Python, SQL, Power BI, AWS, and machine learning frameworks (LSTM, GRU, ARIMA, XGBoost),I specialize in predictive modeling, business intelligence, and deploying machine learning solutions that deliver measurable business impact.
My graduate research focused on AI-driven sales forecasting, improving retail prediction accuracy by 30% through advanced time-series and deep learning models. This, combined with my prior work in IT systems and database management, gives me a unique ability to bridge technical expertise with business insight.
I'm open to opportunities in Data Science, Data Analysis, Business Intelligence, Cloud Analytics, and Data Visualization. .