Zixian He

* Analyzed daily operational data to uncover trends, anomalies, and performance gaps, enabling data-driven decisions and process improvements. * Developed volume forecasts based on historical patterns to optimize workload planning and resource allocation. * Improved data quality through systematic validation and correction, ensuring accurate reporting and supporting reliable business insights.

• Unified HR, finance, and operations data into a single reporting pipeline to improve cross-department consistency. • Automated ETL and variance checks to flag misclassified expenses and unusual spend spikes, reducing manual review work. • Built Tableau/Excel dashboards showing spend and variance to help leadership spot cost issues and plan resource allocation.

• Developed predictive models in Python using ARIMA and scenario simulation to forecast photovoltaic (PV) energy output and BTC mining profitability, enabling long-term ROI analysis for hybrid renewable–crypto projects under varying load and market conditions. • Led financial due diligence across C&I solar and EV infrastructure projects, improving investment accuracy and ITC eligibility. • Built dynamic financial and cash flow models to evaluate ROI, tax incentives, and capital structures across multi-phase clean energy projects, directly supporting over one billion in investment decisions while ensuring compliance and forecast integrity. • Designed and implemented an end-to-end CRM workflow system using Google Workspace and Apps Script, enabling automatic intake of client-submitted forms, document categorization, phase tracking, and notification triggers. • Engineered a role-based compensation database with standardized inputs and built-in validation logic, improving payroll data integrity and reducing manual review cycles. • Created a real-time billing audit platform using AppSheet and Google Sheets, featuring automated ID generation, credit tracking, status updates, and date-based filters—enhancing billing traceability and reporting efficiency.

• Analyzed and consolidated information from 26 regulatory documents to classify and standardize personal security data, ensuring compliance with data privacy laws and internal policies. • Designed and implemented an Entity-Relationship Diagram (ERD) in MySQL to standardize data structure, reducing data inconsistencies and improving data collection accuracy by approximately 15%. • Automated workflows using Python and MySQL, streamlining data validation processes, reducing manual efforts by 30%, and minimizing errors. • Conducted extensive data cleaning and validation to address inconsistencies in personal security information, enhancing data reliability, improving compliance reporting, and enabling faster, more accurate decision-making.

• Analyzed sales and booking data to assess post-COVID recovery trends in domestic and outbound travel. • Built time-series forecasting models (ARIMA and regression-based baselines) to project monthly revenue under different recovery scenarios. • Engineered features capturing seasonality, holidays, and policy-related demand shifts to improve forecast stability. • Conducted scenario analysis to estimate revenue impact under conservative and optimistic recovery assumptions. • Evaluated model performance using back-testing and error metrics (e.g., MAE, RMSE) to ensure reliable projections. • Delivered data-driven insights and visual summaries to support pricing and operational planning.

Honors: Dean’s List 2021

Honors: The Dean’s List (2019&2020), the President’s List (2019)