Ken Tan

I am a passionate and results-driven data scientist with over 4 years of experience in full-stack development and 3+ years specializing in machine learning and AI. My expertise spans across Frontend Development (ReactJS), Backend Development (Python, Django, SQL, and NoSQL DB), containerization and orchestration (Docker, Kubernetes), and deployment tools (Terraform, Ansible). I have a solid track record of building and deploying scalable solutions, with a focus on automation, CI/CD pipelines, and cloud-based systems.

With a Master’s Degree in Data Analytics from Singapore Management University and a Bachelor’s Degree in Electrical & Electronics Engineering from Nanyang Technological University, I bring a strong academic foundation to my work. My skills in Python, machine learning frameworks like TensorFlow and scikit-learn, and a deep understanding of data analytics and engineering enable me to drive impactful insights and innovative solutions.

I am particularly passionate about leveraging technology to solve complex problems and drive business value, whether it’s through data-driven decision-making, developing intelligent systems, or enhancing user experiences. I am continuously seeking opportunities to learn and grow, particularly in the areas of AI/ML, cloud computing, and DevOps practices.

I am currently open to new opportunities where I can contribute my skills and experience in data science, machine learning engineering, or software development. If you're looking for someone who is committed to excellence, innovation, and teamwork, I would love to connect!

INLAND REVENUE AUTHORITY OF SINGAPORE (IRAS) – Singapore                                                                       Jul 2022 - Jan 2024

Data Scientist

• Developed both supervised & unsupervised machine learning models on various platforms such as JupyterHub, JupyterLab, VS Code etc. using Python & its libraries that improved work performance in the Tax Recovery Division.

• Developed a supervised machine learning model which involved using Logistic Regression, Random Forests and Decision Tree with precision, recall & f1-scores of 67%, 66% & 66% that predicted high-risk late/non-tax filers which tax officers focused their tax recovery on resulting in a 27% increase in taxes recovered in the 2022 financial year.

• Developed a supervised XGBoost model for each tax type to recommend the agent (e.g. bank) to appoint when recovering taxes from taxpayers which resulted in a 31% increase in taxes recovered in the 2023 financial year.

• Performed unsupervised Topic Modeling on the data of contact notes from taxpayers by carrying out EDA, masking, tokenization, lemmatization, K-means clustering, NLP & LDA on said data resulting in the identification of common reasons for tax default on a weekly, monthly & yearly basis.

PRICEWATERHOUSECOOPERS (PwC) – Singapore                                                                                                      Jul 2021 - Jun 2022

Software Engineer

• Developed full stack software/web applications for tax data processing by coding in HTML/CSS for the front-end user interfaces, C# for the backend site functionalities & SQL for the databases of the websites which allowed our clients to calculate their tax amounts with ease and efficiency.

• Programmed in languages such as SQL & SAS on Microsoft SQL Server Management Studio & SAS Enterprise Miner for data mining & data extraction which resulted in the ease of data access for the operations team.

Master of IT in Data Analytics