Designed, implemented, and migrated legacy data into CRM/Ticketing Database 'Tessitura,' containing 100k customer records. Managed 15 dashboards and 100+ reporting structures, achieving annual fundraising goals of $4M and ticketing sales of $1M.Established a Database Governance Committee, implementing robust data governance standards, ensuring data integrity, security, and compliance with industry regulations (e.g., GDPR, TCPA, PCI DSS). Fostered a culture of responsible data management across financial and customer data.Conducted training and onboarding for new staff, and organized bi-monthly professional development symposiums. Effectively communicated complex analytical results to both technical and non-technical stakeholders. Managed multiple employees, assigned and tracked tasks and projects.Manage and monitor $150k CRM and software budget, while also overseeing database security permissions, CRM integrations, and partner relationships to ensure the integrity, accuracy, and compliance of each department's financial and customer data.Delivered a conference presentation at TLCC 2022 in Colorado, showcasing the optimization of the moves management process, operations, and a quantitative approach to maximizing pipeline of qualified donors through internal and external data analysis, resulting in a 500% increase in the flow of major prospect donors and $1M in new donations over the past few years.

Developed an AI chatbot for educational helpdesk tool. Ingested and cleaned client data for dynamic responses using semantic search, retrieval augmented generation, and OpenAI API. Reverse-engineered schema from client's SSMS SQL database .bak file, deciphering table structures and data relationships. Delivered a functional prototype within a 6-week timeframe.

Highly immersive 500+ hr Data Science fellowship: hands-on experience with Python, R, SQL,ML, big data, visualization, web scraping, stats, and analytics.Completed data science projects include:PROJECT 1: Sourced data from 1,000 EV car trips over 1 year, proving a 58% and $1,400 annual savings compared to gas vehicles. Discovered negative correlation between outside temperature and battery efficiency, confirming a 23% and 100-mile range reduction below 40°F.PROJECT 2: Analyzed 16,000 stock trades by 200 US Congress members (2014-2023), demonstrating an average 8% higher return than S&P500. Highlighted Republicans' trading strength over Democrats and identified individuals with up to 270% returns on certain trades.PROJECT 3: Optimized Machine Learning models for 92% accurate home price prediction in Ames, Iowa. Identified neighborhoods where upgrading kitchen quality from 'Average' to 'Excellent' led to up to 8% increase in sales price, aiding profitable investments.PROJECT 4: Developed cost-efficient real estate investment tool leveraging business's local demographics and location as market trend indicators. Utilized existing insights from proprietary information, reducing data acquisition expenses.Topics included statistics, simple/multiple linear regression, logistic regression, generalized linear models, decision trees, random forests, gradient boosting, Ridge and Lasso regression, support vector machines, neural networks, clustering, k-nearest neighbors (KNN) , and other machine learning algorithms.

Directed university ensembles while concurrently performing as a professional violinist in 4 Symphony Orchestras and managing a thriving string quartet wedding business.

Directed grade school level orchestra for 6 ensembles and 200 student musicians