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.

• Conducted market research to determine if there was a demand for a subscription-based grocery delivery service in our target market.

• Validated qualitative assumption through a fake door test.

• Identified our target audience by segmenting our user base in buckets according to price-sensitivity and frequency of order.

• Defined the scope of the subscription-based service, including the possible price points to test, order frequency, payment service, and so on

• Estimated the financial viability of the subscription-based service, taking into account revenue, expenses, and margins.

• Analyzed the cost of delivering groceries, the cost of acquiring and retaining customers, and the impact on existing revenue streams. Supported the assessment of the technical feasibility.

• Launched Everli Plus and co-implemented the marketing strategy to promote it and attract subscribers through multiple channels and monitored the success of the initiative and collected customer feedback to improve the service