A career built at the intersection of models, markets and people.

Owned two ML-backed product bets: a new in-app advertising surface and an internal platform that made model workflows easier for operators to use.

• Launched in-app advertising as a new monetization line, aligning product, data science, engineering and commercial teams.
• Built a separate ML application platform used by 100+ daily users and delivering approximately $500K in annual savings.
• Translated model behavior into operator-facing workflows, adoption metrics and a roadmap teams could execute.

Turned experimentation and traffic intelligence into repeatable product systems for advertising teams.

• Shipped a B2B traffic optimizer that generated $35M in revenue while reducing costs by 70%.
• Launched an experimentation platform with role-based access controls, supporting 30 experiments in six months.
• Supported APAC go-to-market with customer and market insight grounded in product performance.

Built the ML and data foundation that later informed how I lead products.

• Developed a bid-prediction model powering an advertising pricing optimizer that generated $80M over 12 months.
• Built and operated large-scale data pipelines processing terabytes per day in HDFS.
• Worked close to the model, infrastructure and business logic—experience I now use to set realistic product direction.

Used buyer research and go-to-market strategy to shape adoption for an LLM platform serving government and enterprise contexts.

• Led buyer and market research to clarify high-value use cases and decision criteria.
• Developed go-to-market direction that contributed to $700K in new ARR.
• Bridged technical platform capabilities with the language, proof points and workflows buyers needed.

Applied computer vision and color science to flagship mobile experiences at global scale.

• Contributed to Night Mode work on a device line associated with $1B in sales.
• Developed color-tuning capabilities shipped across 25M+ flagship devices.
• Learned to balance research quality, hardware constraints and the expectations of a mass-market product.