Margarett Giang - Senior Data Scientist

Margarett Giang

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Margarett Giang

4.8

Gender

Male

Address

350137, Potong Pasir

Language

English - Advanced

Main expertise

Data Scientists Deep Learning NLP AI Machine Learning Python R C++AWS Azure Docker Cloud

Introduction

Margarett Giang is a seasoned Senior Data Scientist with seven years of experience, specializing in utilizing advanced analytics and technology to address complex challenges. She has strong proficiency in Python, R, Bash scripting (Linux), and C++, alongside expertise in using Git for version control. Her expertise in data visualization is enhanced through tools like Plotly and Streamlit, which enable effective communication of insights. Margarett is highly skilled in cloud solutions, particularly AWS SageMaker, EC2, and Azure, and is adept at managing databases such as MySQL and MongoDB. She is experienced in developing deep learning models and natural language processing (NLP) applications using frameworks like PyTorch and TensorFlow, which optimize analysis and decision-making processes. Margarett excels in high-pressure environments while managing international teams and possesses a solid foundation in statistics, experimental design, hypothesis testing, and system design. Proficient in English (IELTS 6.5) and French (DELF B1), she effectively communicates with colleagues and partners globally. Margarett is eager to embrace new challenges and contribute to the growth of the organization.

Project list

Evonik SEA - Singapore

2022

Involved in various projects that require collaboration with global teams and the implementation of advanced data and machine learning techniques.

Coach and train junior and fresher members to become competent scientists. Work with stakeholders, data engineering team, and other managers in Singapore and Germany to understand project logic, features, requirements, and scope. Derive data insights from current datasets and strategize future data analysis. Finetune and deploy models for name matching, GPT-3, GPT-4ALL, and LLM NLP, as well as multi-tag classification. Refine zero-shot SAM, object detection using YOLO v8, and image classification with Swin Transformer. Manage dashboard creation, a business case website for GS products, and pricing prediction projects concurrently while evaluating the team's performance. Lead sentiment analysis by overseeing data labeling with the Label Studio tool, and implement and deploy NLP projects using Transformers, BERT, PhoBERTs, FastText, a name matching model, and multi-tag classification.

Technologies: Cloud Services: AWS SageMaker, S3, Lambda, IAM, AzureML, Machine Learning and NLP Frameworks: TensorFlow, PyTorch, Scikit-learn, Models: GPT-3, GPT-4, BERT, PhoBERT, FastText, YOLO v8, Swin Transformer, Databases: MySQL, MongoDB, Data Analysis Tools: Pandas, NumPy, Matplotlib, Plotly, Programming Languages: Python, R, C++, Web Development Frameworks: Flask, Django, Containerization: Docker, Labeling Tools: Label Studio, Project Management Tools: Jira, Trello, Communication Tools: Slack, Microsoft Teams, Data Visualization Tools: Tableau, Power BI, Streamlit

Advanced Computer Vision Projects - Singapore

2022 - 2023

In this project, various computer vision solutions were developed and implemented using deep learning techniques. The project focused on building and optimizing models for tasks such as object detection and classification, with applications in automotive damage detection and more.

Work closely with the Board of Directors to build and expand the Computer Vision team up to 6 members and evaluate team performance. Lead the team to execute computer vision projects, following Scrum methodologies, including daily meetings and weekly progress reports. Design machine learning pipelines and deploy projects using AWS SageMaker, S3, and Lambda functions. Apply deep learning technologies such as Mask RCNN, Detectron2, and transfer learning for car detection, panel detection, and car damage detection. Implement object detection solutions using YoloV5 and EfficientNet.

Technologies: Cloud Services AWS SageMaker, S3, Lambda, Programming Languages: Python, R, SQL, Models and Libraries: Mask R-CNN, Detectron2, YOLOv5, EfficientNet, Techniques: ML Pipeline Design, Transfer Learning, Deep Learning Model Optimization, CI/CD Pipeline Automation

Acoustic Machine Learning and Computer Vision Project - Singapore

2021 - 2022

This project focused on developing computer vision applications and sound event detection using advanced machine learning techniques.

Work closely with the Board of Directors to establish and expand the Computer Vision team to 6 members, and evaluate the team's performance. Lead the team in executing computer vision projects, utilizing Scrum methodology with daily meetings and weekly reports. Develop and implement sound event detection and car make audio classification using EfficientNet, transfer learning, and MFCC. Perform feature extraction and model development using machine learning classification techniques such as SVM, Random Forest, logistic regression, neural networks, ensemble models, and Bayesian models.

Technologies: AWS, EfficientNet, MFCC, Support Vector Machine (SVM), Random Forest, Logistic Regression, Neural Networks, Ensemble Models, Bayesian Models, Agile/Scrum methodologies, Python, Pandas, NumPy

GENE Project - Vietnam

2019 - 2021

The GENE Project focuses on implementing machine learning models for disease classification and designing data pipeline architectures.

Build and manage a team of 5 members to implement projects. Follow scrum meetings. Present and share knowledge. Create an attractive and challenging environment for the team. Implement disease classification models to classify signals into 3 types of diseases using Logistic Regression, SVM, and Random Forest. Fine-tune machine learning models. Design parallel data pipeline architecture for running Big Data workflows related to Human Genome WGS. Conduct data analysis and data visualization. Engage in parallel programming with Nextflow tools. Create an automation system to run pipelines and manage CI/CD automation. Expert in Docker and microservice architecture.

Technologies: Python, R, Agile/Scrum methodologies, Logistic Regression, Support Vector Machine, Random Forest, Data Pipeline Architecture, Nextflow, Big Data, Data Analysis Tools, Data Visualization Tools, Docker, Pytest, Microservices Architecture, CI/CD Automation

WonderSprout - USA

2017 - 2019

A collection of projects focused on implementing advanced AI technologies and web development solutions.

Implement projects: Face recognition, KYC, and Age/Gender prediction. Speech recognition using Spectrogram image processing. Image recognition using Deep Learning techniques, including the MFCC method. Design and develop a website using Django to showcase a chat bot. Create and manage chat bot operations using Watson Assistant.

Technologies: Python, R, Face Recognition, KYC (Know Your Customer) System Development, Age and Gender Prediction Modeling, Speech Recognition, Deep Learning for Image Recognition, Django, Watson Assistant, API Integration, System Deployment, FastAPI

Project list

Education

Expertise