● Managed over 20 million impressions per week on the homepage using recommendation engine.● Owned and optimized the development of more than 10 recommendation algorithms, and directed the product strategy based on data-driven performance metrics.● Assisted Product Manager in managing the end-to-end product development process for AI products.● Reported performance metrics to C-level stakeholder on weekly basis, and translated stakeholder's vision into product implementation context● Worked closely with data scientists, data engineers, and content team (labelers) to identify, resolve problems, and developed solutions.

● Developed an automation program to gather product insights by collecting and analyzing over 100,000 reviews from the Google Play Store, App Store, and Twitter.● Assisted the Senior Product Manager in managing the end-to-end product development process for the payment feature● Created product requirement documents (PRDs) for new payment and pay-later product initiatives.● Utilized decision support system models, such as the Analytical Hierarchy Process and Simple Additive Weighting Model, to prioritize sprints and features.● Collaborated with cross-functional teams including engineering, design, and customer service to ensure successful product launch and delivery.● Monitored and analyzed key performance metrics, to make data-driven decisions and improve the product performance.

● Conducted consumer journey analysis research using Twitter data to understand the needs and pain points of education technology consumers.● Utilized Tweepy, Latent Dirichlet Allocation Algorithm, and Python to mine, clean, and analyze data.● Conducted market research and identified opportunities for unique selling points and market positioning for a new product (Edspert.id)● Provided insights and recommendations to the product development team based on research findings.