AREEJ OBARAY

• Led an initiative to address inventory imbalance by closely analyzing historical sales trends, promotional timing, and
customer demand patterns. Working closely with cross-functional teams, I introduced a more data-driven approach to
planning and inventory management.
• Improved forecast accuracy by 20% by integrating multi-year sales trends, promotional data, and seasonality into
predictive models using time series analysis and regression techniques.
• Developed and maintained Power BI dashboards to visualize sales trends and identify opportunities, leading to a 12%
increase in profit.
• Architected comprehensive Power BI synthesizing BigQuery and Excel data, establishing unified reporting systems that
strengthened cross-functional decision-making.
• Optimized sales reporting workflows through advanced SQL implementations, markedly decreasing manual processing
time while accelerating stakeholder response capabilities.
• Conducted quarterly dashboards sales trend analysis, providing actionable insights into inventory planning and stock
allocation, resulting in a 15% reduction in excess stock.
• Applied machine learning models (specifically Random Forest and ARIMA) to improve demand forecasting, reducing
forecast errors by 15%.
• Leveraged NLP techniques (sentiment analysis) to analyze customer feedback data from online reviews and surveys,
identifying trends that enhanced product recommendations and marketing strategies, leading to a 10% improvement in
customer satisfaction scores.

• Conducted in-depth data analysis on COVID-19 trends across campus and regional populations using Python (pandas,
matplotlib) and SQL, supporting campus health strategy and safety protocol planning.
• Developed comprehensive Power BI dashboards for tracking COVID metrics across campus, enabling real-time monitoring
and informed protocol adjustments.
• Implemented real-time data refresh in Power BI dashboards to provide up-to-the-minute insights on the pandemic
situation, facilitating timely decision-making.
• Applied time-series forecasting models (ARIMA) to predict future case spikes and inform resource planning.
• Automated data pipelines for COVID data collection from internal systems and public health APIs, reducing data
processing time by 30%.
• Supported faculty research by cleaning and preparing COVID-related datasets for academic publications and policy
analysis.

• Led data analysis efforts to optimize supply chain models, resulting in a 25% improvement in revenue.
• Designed interactive dashboards using Tableau to track sales performance and identify growth opportunities.
• Integrated Oracle ERP Cloud with Supply Chain systems, improving inventory accuracy and reducing lead times by 10%.
• Collaborated with stakeholders and provided data-driven recommendations that improved cross-selling strategies and
increased revenue by 20%.
• Developed predictive models for sales forecasting, resulting in a 15% reduction in excess inventory and stockouts.
• Led a team of 3 analysts to streamline reporting processes, saving 60 quarterly hours of manual data processing.
• Conducted competitor analysis on product pricing, contributing to a 10% increase in sales.
• Gathered business requirements through interviews, surveys, and observing from account managers, and conducted
controlled brainstorming sessions with project focus groups and documented them in the Business Requirement
Document.
• Documented all data mapping and transformation processes in the Functional Design documents based on business
requirements.