A Timeline of My Career

I have been focused on investments in AI startups. More details to come.

In my fourth chapter at Spotify, I was responsible developing reliable and scalable backend systems.For internal teams, I was focused on creating features to enable them to scale and test new AI features. For users, I ensured safeguards were built so users have a consistent and ubiquitious experience.

• Modularized the page building and loading to scale new AI features
• Improved data modeling to enable user feedback to instantly be updated in their personalized experience
• Scaled API development so teams could abstract away code maintenance and decrease complexity
• Automate workflows with AI to accelerate hypothesis testing and iteration

In my third chapter at Spotify, I worked to help creators grow their audience and monetize their content through Spotify promotions. My role was two-fold in this team: expand the suite of tools available to creators so they may customize their campaigns, and also to find quality users to promote to in order to build a loyal fanbase for future monetization.

• Grew non-music consumption and activations significantly from 2023-2025
• Developed a user-targeting machine learning model for inorganic recommendations of podcasts, audiobooks, and video content
• Worked with music business teams to develop ads packages to sell to creators to help them grow their fanbase and following

In the second chapter at Spotify, I moved away from engineering and towards the product & business. I scaled the LTV metric to enable marketing and business teams this metric and signal into their decision-making processes, allowing the company to save $XX million in marketing spend and better negotiate with original equipment manufacturers.

I started my career at Spotify as a software engineer, developing a long-term user retention model to measure user lifetime value. I worked with data science teams to create signals indicative of retention and churn from Spotify, empowering teams to AB test features with enhanced understanding of user impact.

• Coded primarily in Python for Machine Learning development; Java + Scala for backend
• Utilized Google Cloud Platform for data storage and processing, compute, and other ML tasks
• Built a Markov-Chain inspired model to simulate user retention as states

I was accepted into Cornell for a masters degree in Computer Science. However, shortly after starting pre-courses, I received my job offer with Spotify - which led to me dropping out.

Following Paragon One, I joined to PwC as a management consultant specializing in financial services. I worked with large investment banks, hedge funds, and Fortune 500 companies on managing regulatory and compliance requirements.

• Worked primarily with data science teams to develop data models and automation tasks
• Built dashboards and monitoring tools to improve oversight and guardrails
• Developed proof of concept models for AML, KYC, and fraud detection

I joined Paragon One (now Extern) to help develop its core business. My role was to understand the motivation behind users wanting to be mentors, and create a feedback loop that would increase engagement.

• Gamified the core platform and dashboard using Yu-kai Chou's Octalysis framework
• Worked with developers to design, build, and test new features on the core site
• Assisted in migrating tech stack from EmberJS to ReactJS

Upon graduating from high school, I moved to New York City for NYU. I completed a bachelor's degree in Mathematics and Computer Science.

I moved to New Hampshire to attend Phillips Exeter Academy, a boarding school in New Hampshire.

I spent the majority of my youth growing up in Bangkok, Thailand. My family is Taiwanese, and only moved to Thailand for work.