I specialize in improving operational systems through automation, data analysis, and process optimization. My work focuses on building practical, reusable solutions that reduce manual effort, improve data quality, and support better operational decision-making. This portfolio highlights real-world automation and analytics projects developed to solve operational business problems.
Python • SQL • R
Pandas • Matplotlib • Seaborn
Tableau • Excel • Google Sheets
Jupyter Notebooks • JavaScript • CSS • HTML
- CodeCademy - Business Intelligence Data Analyst Certificate
- CodeCademy - Data Scientist: Analytics
- Google - Data Analytics Career Certificate
- B.A., Sociology - Hiram College
Operations Project Manager
Audience Rewards (January 2024 - Present)
- Lead weekly validation across 35K-200K+ customer transactions, loyalty points, and 50+ partner data pipelines to ensure reporting integrity and reduce financial risk.
- Developed Python automation tools that reduced weekly reconciliation time by 80%, standardized validation workflows and significantly decreased manual effort.
- Investigated and corrected 1M+ missing loyalty points by analyzing transactional discrepancies using Python, mitigating financial and customer impact.
- Perform weekly and ad hoc analyses of sales, customer behavior, and promotional performance to support operational planning and business decisions.
- Provide analytical support for marketing experiments by delivering campaign performance data that informs promotional strategy and business decisions.
Clinic Launch Manager
Bond Vet (March 2021 - September 2023)
- Led on-time operational launches for 10+ new veterinary clinics, coordinating 5-10 cross-functional team members monitoring and multiple external partners while monitoring KPIs and post-launch performance.
- Developed operational dashboards used by 10+ stakeholders to monitor execution, issue trends, and financial performance metrics.
- Conducted root cause analysis on operational bottlenecks and implemented process improvements.
The weekly reconciliation process required manually comparing multiple operational data sets, validating promotional programming, and updating Excel audit workbooks. The process consumed over three hours each week and relied on repetitive manual work.
Designed and built a reusable Python automation that integrated Databricks exports, Google Sheets, and existing Excel workbooks into a standardized reconciliation workflow.
Python Pandas OpenPyXL Excel Jupyter
- Automated workbook population
- Date normalization
- Formula preservation
- Cross-sheet validation
- Automatic summary reporting
- Reusable workflow
- Reduced weekly processing time by approximately 83%
- Standardized validation across recurring audits
- Improved reporting consistency
- Reduced manual effort
Marketing needed timely insight into promotional campaign performance, customer purchasing behavior, and campaign profitability in order to evaluate future promotional strategy.
Developed a reusable Python analytics tool that transforms raw transaction data into business ready reporting including ROI calculations, ticket yield analysis, average ticket price, purchase trends, and campaign summaries.
Python Pandas Excel Jupyter
- ROI calculations
- Yield analysis
- Utilize internal price structures to calculate cost based on points
- Reindexing days for day-of-week trend analysis
- Branded summary generation
- Reusable analysis template
- Utilized more than 200 times in calendar year to support promotional analysis
- Reduced manual reporting effort
- Standardized campaign evaluation
- Improved turnaround time for marketing requests
When approaching an operational challenge, I generally follow the same process:
- Understand the business problem
- Identify inefficiencies, repetitive manual work, and process bottlenecks
- Validate data quality before building reports
- Develop reusable solutions that minimize maintenance
- Measure the operational impact through time saved, consistency, and improved business decisions