Profile of Cheng Yen Goo

Cheng Yen Goo Data Scientist
0 years experience 0 projects worked Malaysia
RM5.00 /hr RM0 earned
AI TrainerC++ Programming

I am a Data Scientist with a solid background in developing Python-based data pipelines and creating LLM-driven reporting systems. My experience includes optimizing time-series anomaly detection models using Multivariate Autoencoders and advanced signal processing techniques. I have successfully integrated MCP with Trino for large-scale data processing, effectively addressing challenges like data truncation and pipeline scalability. With a keen eye for detail and a commitment to extracting actionable insights from complex datasets, I bring both creativity and technical acumen to my projects. If you’re looking for someone with hands-on experience in data science who can enhance your data processes and drive impactful results, I would be thrilled to explore your project further.

Work History (0)

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Work Experiences

  • Data Scientist Intern

    Seagate Technology

    June 2025 - December 2025

    – Designed and enhanced AI-driven data pipelines for automated report generation, integrating structured (.csv) and unstructured (.eml) data sources with LLMs for analytical insights.
    – Conducted data profiling, quality assessment, and cleaning strategies, addressing real-world challenges such as large input truncation and incomplete LLM outputs through prompt engineering and data chunking.
    – Researched and applied Model Context Protocol (MCP) and evaluated its integration with Trino to improve large-scale data accessibility, query performance, and system reliability.
    – Explored and tested Agentic AI systems and sandbox environments, developing markdown-based workflows for autonomous data processing, reporting, and performance evaluation.
    – Implemented workflow automation and orchestration using AI-assisted sandbox tools and Airflow DAG concepts to enable repeatable, traceable data operations.
    – Applied time-series analysis and anomaly detection techniques, including denoising strategies and Multivariate Autoencoder (MVA) models to improve sensor data quality and detection accuracy.
    – Studied and adapted existing wafer anomaly detection solutions, refining preprocessing, model configurations, and thresholds to support shadow deployment readiness.

Education

  • Bachelor's Degree in Data Science

    Tunku Abdul Rahman University of Management and Technology (TAR UMT)

    November 2022 - December 2025