Work Experiences

E-commerce store / digital brand selling my own line of custom fútbol (soccer) jerseys.

Manage all aspects of business and product's lifecycle:

• Prototyping: custom jersey designs using Photoshop, Blender
• Marketing + content creation: social media for building customer base, testing jersey ideas, gauging demand, and funneling to Shopify store. Continually refining and launching new products. Tools used: Google Ads (Pixels, AdSense), A|B testing, custom affiliate program.
• Production + logistics/fulfillment of jerseys: researching product material (polyester, % blends, etc.), negotiating contracts with suppliers and garment factories, handling international courier/fulfillment services all the way through product arriving at doorstep of customer.

To date:

• Social media presence: 30K+ combined following across IG, TikTok, YouTube, Facebook and a 1K+ email newsletter subscriber list.
• 5000+ customers across 89 countries, monthly run rate of 150 units, 4.5+ (5 scale) customer review rating on store where I host my products, testimonials, and customer reviews.

Since expanded into custom client orders for influencer brands + charity matches.

• June 2022: Began freelance client work, providing full kit design + supply for charity matches.
• See my documentation for my work on the TikTok vs YouTube Influencer match here.

Links

• Online store: 🔗 https://scorphx.shop
• Scorphx's USPTO Trademark (TSDR).

• Deployed REST APIs for payment microservices to AWS (EC2 + EKS), using Docker containers for processing transaction volumes.
• Implemented continuous integration practices for builds (CircleCI) and unit tests (pytest). Refined UI prototypes and aided senior engineers in optimizing validation logic for enhanced responsiveness across Payment API microservices.
• Attended daily standups and retrospectives, gaining strategic exposure to Visa's massive transaction processing systems.

• Explored datasets of Prime Video member browsing preferences, analyzing scroll behavior, time spent by genre, and bounce rates (Apache Spark, AWS Glue).
• Collaborated with the data analysis and model training team to refine AI/ML models (scikit-learn, AWS SageMaker), leveraging insights for personalized content recommendations, resulting in 8% increase in watch time during controlled A/B testing.
• Team collectively achieved a 15% reduction in data processing time over course of 10 weeks.