Summary

• 9 years experience of large scale AI product building, and is capable of building product from scratch.
• Extensive problem solving experience for data science/engineering, and familiar with transferring real problem into requirements and solution planning.
• A well-rounded engineer in data project who bridges the gap between scientists and engineers.
• A pragmatic and ownership driven person, experienced with gap analysis, migration plan and release management.
• Leader of documentation and process, mentor of junior engineers.
• Product-driven mindset, learning new tech. and use them to build side projects continuously (chatbot, blockchain, ..., etc.)

Expertise fields:

• ML Ops / Data Engineering: This is my strength.
• Data Science/Modeling: Not using the cutting edge technologies in the work, but it can be swiftly brought up to speed. (it's my master degree).

Ad Product Technical Debt Burn Down '18Q4

Automatical Refund System '17Q2 ~ '18Q2

Saved more than 10 millions TWD dollars for our business as well as tremendous human effort, milestones include:

• Automate process to save support team and CM team's human effort (17'Q2)
• Eliminate major data discrepancy (17'Q3)
• Support various timezones, formats and make debug efficient. (17'Q4)
• Different dimension breakdown and co-work with F2E to build a new UI (18'Q1)

Pipeline Reconstruction and Migration '17Q2 ~ '17Q3

• Reconstruct ad-hoc pipeline and improve it by applying unit test, migrating DB, code-refactoring, and migrating to Jenkins.
• Co-work with team members to migrate critical production pipelines to Airflow, till 2019Q1, more than 20 data pipelines are operated by Airflow.

Improve ML Model Performance '16Q2

• Improve high quality inventory discovery by embedding inventory as vector: precision achieve 79% from 5.3%, volume increased to 12.8x
• Extend CPA model to different ads vertical: CPA reduced to 68%, volume increased to 2.4x