My favorite thing about H\FC is the genuine commitment to bridging the opportunity gap for college students. I've been consistently impressed by how H\FC doesn't just talk about helping students but actively creates pathways through your gig platform. What stands out most is the community you've built - the free advice I've learned and the amazing people I've been connected to through events you guys have hosted. The way you guys truly care about helping college students get jobs and experience when the odds don't seem like they are in our favor is refreshing in the current overwhelming job market. I especially appreciate your focus on changing the narrative for the way small businesses and startups hire students by fostering mutually beneficial relationships. As someone navigating the challenges of transitioning from college to my career, I've found H\FC's approach be both practical and empowering by providing real opportunities to build skills and connections. The platform doesn't just list opportunities but fosters a supportive incubator where students can thrive and grow professionally while still in school.

To identify potential retail partners for AHYUN (ahyunskin.com), I would take a strategic, data-driven approach that aligns with the brand's skincare positioning and growth objectives. First, I'd conduct comprehensive research on AHYUN's brand identity, unique selling propositions, target demographics, price points, and current distribution channels. Understanding these elements would be crucial for finding retail partners with complementary values and customer bases. I would then implement a multi-faceted research strategy: Competitive analysis: Identify where similar Korean skincare brands with premium positioning are successfully retailing. This would provide insights into channels already receptive to products in AHYUN's category. Consumer behavior mapping: Research where AHYUN's target customers typically shop for skincare products, considering both physical locations and online platforms. Retail landscape assessment: Create a tiered list of potential partners categorized by: Premium department stores (Nordstrom, Bloomingdale's) Specialty beauty retailers (Sephora, Ulta, Credo Beauty) Emerging online platforms specializing in K-beauty or clean skincare Boutique retailers in key metropolitan markets with high concentrations of AHYUN's target demographic For each potential partner, I would analyze: Alignment with AHYUN's brand positioning and values Customer demographics and overlap Merchandising approach and how AHYUN would be showcased Minimum order requirements and financial terms Marketing support and promotional opportunities Growth potential and expansion plans I would prioritize retailers based on strategic value, creating detailed profiles of the top prospects with specific outreach strategies for each. The final deliverable would be a comprehensive retail partnership roadmap with tiered targets, potential entry points, and projected timeline for approaching each opportunity.

During my time with Biola University's Quantitative Consulting Center, I worked on a data management project for the track team that demonstrates my commitment to data accuracy and integrity. As detailed in our 'Performance Monitoring Framework' report, I partnered with another analyst to clean and structure years of track performance data for Coach Johnson spanning from October 2020 through April 2025. The challenge was particularly complex because we were dealing with multiple data sources: handwritten practice journals, Excel spreadsheets with meet results, and time trial records—all in inconsistent formats with varying levels of detail. With data collected by different coaches over several years, maintaining accuracy was paramount for the performance tracking tools we were developing. To ensure data cleanliness and accuracy, I implemented several key strategies: First, I standardized all athlete names across entries to eliminate inconsistencies in how names were recorded, which was essential for accurate performance tracking over time. Second, I normalized all time formats into a consistent unit (seconds), converting formats like 1:01.12 into 61.12 seconds. This standardization was crucial for accurate trend analysis and comparison across different events and time periods. Third, I created a structured, tab-delimited format that organized every data point by specific parameters: athlete name, date, event class, workout details, distance, repetition number, and cycle information. This allowed for systematic verification of data completeness. Fourth, I conducted thorough validation by cross-referencing the digitized data with original sources to ensure no performances were lost or incorrectly recorded during processing—a critical step given the volume of manually entered data from scanned PDF files of handwritten journals. This meticulous approach to data management created a reliable foundation for the interactive Excel dashboards our team developed, which allowed coaches to visualize performance trends and make data-driven training decisions. The success of these tools depended entirely on the data's accuracy and consistency, demonstrating the direct impact of proper data maintenance on operational effectiveness.