Portfolio: Analytics In Action

Welcome to my portfolio, where each example highlights how I combine technical tools with industry expertise to solve business-critical problems. I specialize in benefits and compliance data, but my analytical process is transferable to a wide range of organizational challenges.


Preventing Data Overload During Open Enrollment

Problem: Uploading full employee census files during online open enrollment overwhelmed systems, leading to crashes and delays.

Solution: Used Excel and the AbleBits add-on to compare current vs. previous census files, flagging only necessary changes for upload. Developed a repeatable macro-based workflow to streamline reviews.

Outcome: Prevented system crashes, preserved data integrity, reduced manual review time, and protected OE timelines.

Tools: Excel, AbleBits, workflow optimization, delta analysis


Ensuring Accurate Benefit Configuration Before Go-Live

Problem: Config errors during onboarding created risk for IRS/HIPAA noncompliance and delayed participant access.

Solution: Used SQL to audit platform records and Excel validation tools to check eligibility rules across multiple platforms. Identified discrepancies, verified configuration, and built audit templates for future use.

Outcome: Delivered 100% of client configurations on time, fully compliant with regulatory standards, with seamless participant enrollment.

Tools: SQL, Excel (validation & cross-checking), platform configuration


Problem: Participants repeatedly lost funds or faced claim denials due to unclear plan rules.

Solution: Used Python and Excel to analyze escalation logs by plan type, timing, and participant status. Identified patterns in forfeiture and appeal timing, then used findings to inform participant education materials.

Outcome: Reduced repeated errors, escalations, and support time. Enhanced participant education and improved internal handling of complex claims.

Tools: Python (Pandas, Matplotlib), Excel, categorization & timing analysis


Boosting Participant Engagement with Smarter Communication

Problem: High support volumes (1,300+ participant emails, 200+ calls/day) strained the Customer Service team.

Solution: Analyzed support logs to identify repeat issues and developed internal training materials, education templates, and external participant-facing content. Presented findings to leadership and proposed ongoing education meetings.

Outcome: Reduced repeat inquiries, increased participant confidence, strengthened internal consistency, and improved plan utilization.

Tools: Excel (log analysis), content creation, presentation decks, process design


SQL Data Management for Workforce Insights

Context: Academic simulation of employee/department databases for workforce analytics.

Technical Work: Built schemas and queried relationships between employee, department, and salary tables. Used SQL joins, filters, and aggregations to extract trends such as hire dates, department managers, and top surnames.

Tools: SQL (PostgreSQL), schema design, multi-table queries, data exploration


Python APIs for Environmental Planning

Context: Academic project demonstrating API integration, data cleaning, and visualization.

Technical Work: Queried Geoapify and OpenWeatherMap APIs to gather weather/location data, cleaned datasets using Pandas, stored API keys securely, and built visualizations for trend monitoring.

Potential Use: Supports operations and logistics planning by providing real-time geographic/weather data.

Tools: Python (requests, Pandas, Matplotlib), secure API handling


Drug Efficacy Study Using Python

Context: Academic case study using real biological datasets to assess treatment outcomes.

Technical Work: Visualized tumor growth in mice across drug regimens. Used statistical tools to measure treatment effectiveness, identify outliers, and test significance.

Potential Use: Showcases ability to model and interpret health-related datasets in a rigorous analytical setting.

Tools: Python (Pandas, Matplotlib, SciPy), boxplots, correlation, regression


SQLAlchemy + Python Visualization

Context: Combined SQL querying with Python-based visual storytelling.

Technical Work: Used SQLAlchemy to query relational databases, organized results in Pandas, and created visualizations to identify key trends.

Tools: SQLAlchemy, Python (Pandas, Matplotlib), data pipeline integration