I combine finance operations, analytics, automation, and applied AI to solve real business problems.

I’m a Financial Billing Analyst II with 15+ years of experience across finance, accounting, billing, and business operations. I build practical analytics and automation solutions that support better decisions and more efficient workflows.

I’m currently completing an M.S. in Data Science.

• Finance and billing analytics
• AI-assisted analysis and decision support
• Business process automation
• Reporting and KPI development for operational and leadership use

Problem: People need structured support for complex decisions, not generic AI output.
Approach: Designed a guided flow from assessment and profile context to personalized outputs.
Built with: Streamlit, hybrid retrieval, RAG, FLAN-T5 experimentation, prompt/context engineering.
Result: Working prototype and evolving case study focused on practical, human-centered AI use.

Problem: Understand workforce trends and automation impact using public data.
Approach: Prepared and analyzed BLS/O*NET data, then translated findings into clear reporting.
Built with: Python, Power BI.
Result: Decision-friendly visuals and trend insights.

Problem: Create consistent, repeatable Excel outputs from paginated reporting workflows.
Approach: Built a reusable reporting/export process for recurring operational needs.
Built with: Power BI Paginated Reports.
Result: More standardized reporting output for business use.

Problem: Explore long-term pattern shifts in historical naming data.
Approach: Cleaned and analyzed multi-decade data with visual exploration.
Built with: Python.
Result: Clear trend narratives from large historical datasets.

• Translate business questions into metrics, logic, and analysis plans
• Build dashboards and reporting that support action
• Use Python/SQL for data preparation, analysis, and prototyping
• Apply automation to reduce repetitive manual work
• Design practical AI workflows with human review in mind

I’m currently deepening hands-on implementation in:

• LLM applications and retrieval design (RAG)
• API-based app structure
• Containerization and cloud deployment basics
• Monitoring and reliability for applied AI workflows

Python, SQL, Power BI, Excel, Power Automate, Office Scripts, R, Tableau, Git/GitHub, Generative AI, RAG

• M.S. in Data Science (in progress)
• B.A./B.S. in Economics and Management, and Accounting

Open to Financial Analyst, Data Analyst, Business Intelligence Analyst, Revenue Analyst, and Power BI-focused opportunities where analytics supports measurable business outcomes.