Tackled the learning issue by implementing various personalized learning styles and improved the product checkout process by decreasing the clicks required for payment from 4 to 2 through A/B testing. This streamlined the checkout process, making it more efficient and user-friendly.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.

We developed and scaled a feature for 3P marketplace sellers to facilitate the adoption of the Pan-European FBA, an EU-wide fulfillment program. The feature is a cost-saving calculator that identifies potential savings for sellers by enabling product listings across the EU, thereby incurring lower fees. This was a problem, as sellers had to align 4 different datasets previously, to get this information before deciding whether to join the program. We utilized a build approach, we started from scratch and led the entire process from inception to the development of the MVP and eventually productionalizing the feature for over 150,000 sellers. This unlocked large holdout brands, such as Instant Pot, and generated $100 million in revenue within a year of the MVP.