Financial Billing Analyst with 15+ years of experience in finance, billing operations, and business reporting, transitioning into Data Analytics and BI Development
I use analytics, reporting, and predictive modeling to turn complex data into clear insights that improve decision-making, reduce risk, and support business growth.
- Data analysis, reporting, and business intelligence
- Power BI development and dashboard solutions
- KPI tracking, trend analysis, and business-focused storytelling
- Predictive analytics, anomaly detection, and NLP
- Data cleaning, transformation, and exploratory analysis
- 15+ years in finance and business operations
- Strong foundation in analytical thinking and data-driven decision-making
- Transitioning into data analytics, business intelligence, and data science
- Experienced in translating business needs into clear reporting and actionable insights
- Master’s in Data Science (in progress)
- Bachelor’s degree in Economics and Management
- Bachelor’s degree in Accounting
- Focus: Machine Learning, Statistics, Predictive Analytics
- 📊 Analyze data to uncover trends, risks, and opportunities
- 📈 Build Power BI dashboards and reporting solutions for business decision-making
- 🧾 Develop KPI tracking and executive-ready reports
- 🤖 Build models for prediction and anomaly detection
- 🧹 Clean, transform, and structure messy real-world data
- Build interactive dashboards and executive-ready reports
- Create KPI tracking and performance-monitoring solutions
- Transform raw data into clear, business-friendly visuals
- Support decision-making with scalable reporting workflows
- Combine analytics and storytelling to make insights easy to act on
Analyzed the impact of AI and automation on jobs using BLS and O*NET data.
- Built Python workflows for data cleaning, analysis, and trend exploration
- Developed Power BI dashboards to communicate workforce patterns and risk areas
- Translated labor market data into business-friendly visual insights for decision-making
Explored 140 years of naming trends to identify cultural and generational patterns.
- Cleaned and prepared long-range historical data for analysis in Python
- Created visualizations to highlight naming shifts, popularity cycles, and social trends
- Used exploratory analysis to turn raw data into a clear narrative supported by visuals
Built an NLP-based project to identify student misconceptions from open-ended responses.
- Applied TF-IDF, embeddings, and classification models to text-based educational data
- Explored how machine learning can surface misunderstanding patterns at scale
- Combined analytical modeling with practical interpretation of model outputs
