Data/ML Engineer & Senior Marketing Professional
- 8+ years of experience in FMCG, E-commerce, Digital Advertising, and SAAS field.
- 4+ years of experience in Data Science/Data Engineering field.
- Designed and implemented business-oriented MLOps architecture.
- Built and monitored Big Data ETL pipeline.
- Data management and governance with Data Lakehouse architecture.
- Programming languages: Python, SQL
- Tools and frameworks: PySpark, Delta Lake, MLflow, Scikit-Learn, Pandas, Flask, Docker, Git
- Familiar with Azure: Databricks, Data Factory, EventHub, OpenAI, Azure DevOps, and more
- Familiar with AWS: SageMaker, S3
- Strong marketing and communication knowledge.
Taipei, Taiwan
[email protected]
+886983460202
- Designed and implemented end-to-end machine learning architectures to predict chemical experiment results, leveraging Databricks, Azure Cloud, and on-premises servers.
- Refactored and optimized the existing data ETL pipeline for real-time processing, using Azure Data Factory, Logic App, and custom Python programs.
- Collaborated with cross-functional teams located worldwide, delivering projects and tasks for diverse business units across different regions.
- Designed data ingestion pipelines from data sources like on-premise databases, Azure EventHub, and APIs, processing 25M raw records update daily, using Azure Data Factory, Azure Function, Python async programming, APIs.
- Introduced Azure Data Factory's parameterized data pipeline to prevent manual tasks, simplify the pipeline structure, and cut down daily ETL processing time by 75%.
- Refactored data transformation program using Databricks and PySpark, reducing processing time from 8+ hours to less than 30 seconds at most.
- Designed ML projects and introduced MLOps to improve machine learning workflow, using MLflow, Pyspark MLlib, Azure Devops, and Databricks CLI.
- Introduced and implemented Databricks Auto Loader as an streaming ETL solution, which cut down processing time by 50% and prevented overheads like manually tracking job checkpoints.
- Served as an in-house Python trainer, training 7-8 members in the data team.
- Developed AI digital ads optimization programs across multiple digital ad platforms, resulting in saving clients' ad spend by 20% and increasing ROAS by 200%.
- Implemented data ingestion from 3 mainstream ad platforms, processing millions of raw records daily.
- Designed data pipelines for ETL, ad bidding calculation, model inference, and applying adjustments to ad platforms, using a combination of crontab, APIs, MySQL, and AWS Sagemaker.
- Implemented a deep learning pipeline for ad audiences recommendation, which included feature engineering, model training, and predicting the suitable audiences among a variety of labeled groups with 80% accuracy, using AWS S3 and Sagemaker and custom Python scripts.
- Leveraged clustering algorithm to simulate human agent ad operation, resulting in 95% similarity with the real actions.
- Built a deep CNN for predicting click through rates of ad creatives with MAE 0.8%.
- Managed Facebook API based software products (Chatbot, Ad Tech, Creative system)
- Arranged Chatbot scenario, conversation flow, and UX for clients' business goals
- Responsible for oversea partnership development and local business development
- Facebook ad operation
Digital marketing and CRM.
Product marketing and management.
- Chinese (Native)
- English: Fluently
- Korean: Intermediate
Data/ML Engineer & Senior Marketing Professional
- 8+ years of experience in FMCG, E-commerce, Digital Advertising, and SAAS field.
- 4+ years of experience in Data Science/Data Engineering field.
- Designed and implemented business-oriented MLOps architecture.
- Built and monitored Big Data ETL pipeline.
- Data management and governance with Data Lakehouse architecture.
- Programming languages: Python, SQL
- Tools and frameworks: PySpark, Delta Lake, MLflow, Scikit-Learn, Pandas, Flask, Docker, Git
- Familiar with Azure: Databricks, Data Factory, EventHub, OpenAI, Azure DevOps, and more
- Familiar with AWS: SageMaker, S3
- Strong marketing and communication knowledge.
Taipei, Taiwan
[email protected]
+886983460202
- Designed and implemented end-to-end machine learning architectures to predict chemical experiment results, leveraging Databricks, Azure Cloud, and on-premises servers.
- Refactored and optimized the existing data ETL pipeline for real-time processing, using Azure Data Factory, Logic App, and custom Python programs.
- Collaborated with cross-functional teams located worldwide, delivering projects and tasks for diverse business units across different regions.
- Designed data ingestion pipelines from data sources like on-premise databases, Azure EventHub, and APIs, processing 25M raw records update daily, using Azure Data Factory, Azure Function, Python async programming, APIs.
- Introduced Azure Data Factory's parameterized data pipeline to prevent manual tasks, simplify the pipeline structure, and cut down daily ETL processing time by 75%.
- Refactored data transformation program using Databricks and PySpark, reducing processing time from 8+ hours to less than 30 seconds at most.
- Designed ML projects and introduced MLOps to improve machine learning workflow, using MLflow, Pyspark MLlib, Azure Devops, and Databricks CLI.
- Introduced and implemented Databricks Auto Loader as an streaming ETL solution, which cut down processing time by 50% and prevented overheads like manually tracking job checkpoints.
- Served as an in-house Python trainer, training 7-8 members in the data team.
- Developed AI digital ads optimization programs across multiple digital ad platforms, resulting in saving clients' ad spend by 20% and increasing ROAS by 200%.
- Implemented data ingestion from 3 mainstream ad platforms, processing millions of raw records daily.
- Designed data pipelines for ETL, ad bidding calculation, model inference, and applying adjustments to ad platforms, using a combination of crontab, APIs, MySQL, and AWS Sagemaker.
- Implemented a deep learning pipeline for ad audiences recommendation, which included feature engineering, model training, and predicting the suitable audiences among a variety of labeled groups with 80% accuracy, using AWS S3 and Sagemaker and custom Python scripts.
- Leveraged clustering algorithm to simulate human agent ad operation, resulting in 95% similarity with the real actions.
- Built a deep CNN for predicting click through rates of ad creatives with MAE 0.8%.
- Managed Facebook API based software products (Chatbot, Ad Tech, Creative system)
- Arranged Chatbot scenario, conversation flow, and UX for clients' business goals
- Responsible for oversea partnership development and local business development
- Facebook ad operation
Digital marketing and CRM.
Product marketing and management.
- Chinese (Native)
- English: Fluently
- Korean: Intermediate