● Collaborated with cross-functional teams to process, clean, and engineer over 100 million rows of vehicle repair data using Python (Pandas and Polars), delivering insights that optimized maintenance strategies.● Designed and implemented clustering models to identify distinct customer vehicle profiles based on geospatial data, improving targeted service offerings.● Conducted predictive analysis using XGBoost and Random Forest algorithms to determine key factors influencing truck repair rates.● Effectively communicated complex findings to non-technical audiences through interactive Tableau dashboards, enhancing decision-making across teams.● Utilized GitLab for streamlined project management and enhanced code versioning within an Agile framework.(*) University facilitated an 8-month project with real-world data sponsored by Daimler Trucks North America.

* Developed and presented data-driven business insights and ROI analyses for audiovisual projects, successfully securing funding for 60% of proposed initiatives.* Conducted exploratory and statistical analysis for film production data, optimizing budgeting and logistics for over 60 projects.* Managed project budgets of over $10,000, ensuring adherence to financial plans and prudent spending.* Assessed in-bound projects and designed pre-production schedules that ensured smooth development of logistics, crew selection, casting and hiring, and timely delivery for 60+ projects.* Managed and led cross-functional teams of up to 30 members, ensuring project goals were met through effective communication and resource allocation.

* Directed press operations, including briefing a 50+ member press pool, training, and assigning assistants to photographers based on technical expertise for 47 game venues.* Led and mentored to a team of 40+ photo operations staff, offering expertise in technical and crisis management decisions at their respective headquarters.

* Established new communications platforms for 120+ health insurance clients, impacting 85% of our clients.* Planned merchandising orders based on historical performance data using average orders, client size, and demand estimations, decreasing the merchandising deployment time in 13%.* Contributed to developing and designing new insurance product by spearheading the creation and measurement of the results of surveys to assess feasibility (Survey completion rate: 70%).