Chee Yee Lim's Personal Website

About

I am a hands-on technical leader dedicated to transforming complex data into actionable business intelligence for global organizations.

Utilizing deep expertise in AI, software development, and cloud solutioning, I actively build alongside high-performing teams to bridge the gap between innovation and execution.

I am passionate about cutting-edge tech, but strictly focused on alignment with business goals to ensure every technical solution drives measurable commercial value.

Experience

July 2023 – Current

Data Science Manager @ Johnson & Johnson, Singapore

  • Served as a hands-on technical lead for a team of 4 data scientists, balancing team leadership with active development to deliver agentic AI workflows and ML-based predictions across 5 production and 3 POC projects for the global finance team.

  • Architected production-grade agentic AI workflows to automate financial processes - including balance sheet reconciliation - utilising LangGraph and Deep Agents for agent harness, backed by OpenAI GPT for large language model (LLM) inference and MLflow for LLM monitoring.

  • Engineered scalable data ETL pipelines on Databricks, leveraging Delta Lake for data versioning and Unity Catalog for data lineage tracking to ensure rigorous data quality management and faster processing speed.

  • Scaled a global demand-forecasting solution utilising Apache Airflow, Polars and Nixtla, interfacing with SAP and Anaplan to create forecasts for 2 million country-SKU combinations, driving the adoption of automated baseline forecasts across 4 key markets due to 9% accuracy improvement over existing forecasts.

  • Championed data science initiatives to business audiences by translating complex technical jargon into strategic business concepts, securing stakeholder buy-in.

June 2022 – June 2023

Data Science Manager @ DHL Consulting, Singapore

Sept 2020 – June 2022

Senior Data Scientist @ DHL Consulting, Singapore

  • Led a team of 3 data scientists to deliver 8 end-to-end data science solutions (5 successfully deployed to production) spanning customer service analytics, customer retention and fraud detection.

  • Secured SGD 570,000 in revenue by driving business development workshops and cross-divisional alignment with regional and country heads, translating client business gaps into 5 funded data science engagements.

  • Architected and deployed a customer service analytics product that ingested multi-channel communication (chats, emails, calls) to surface trending topics and KPIs, significantly reducing response times and staffing costs while improving service quality for our client.

  • Developed high-accuracy NLP engines - including custom intention classification and sentiment analysis models - by leveraging a manually curated dataset of 20,000 labelled customer texts to drive automated, actionable customer insights.

  • Generated SGD 860,000 in annual savings across delivered portfolios while maintaining an exceptional client satisfaction rating, achieving an average Net Promoter Score (NPS) of 68.8.

July 2019 – Sept 2020

Data Scientist @ PatSnap, Singapore

  • Developed a random forest-based model for patent value prediction by integrating novel NLP-based metrics extracted from patent texts with traditional patent indicators.

  • Worked closely with a team of 3 product managers and 2 engineers to ensure the product is developed on time while achieving business goals and fitting into existing IT infrastructure.

  • The patent value product replaced a third-party patent value data provider, which helps our company saves USD 100,000 per year in subscription fee.

Sept 2017 – May 2019

Data Scientist @ Schroders, UK

  • Led the development of the human capital data product to provide summary insights into the board director relationships and career histories for 20,000+ public companies globally.

  • The final product was perceived as ‘a distinct value-add and massive time saver’ by the heads of 3 investment research teams who requested their analysts to use the product as part of their process.

  • Liaised with 9 data vendors and verified the quality of alternative data by checking their data collection and processing methodology, as well as comparing the data with known information.

Education

Oct 2013 – Sept 2017

PhD in Computational Biology @ University of Cambridge, UK

  • Used machine learning techniques to study how stem cells make developmental decisions by analysing terabytes of time-series single-cell expression data.

  • Graduated on-time with 2 research papers and presented a poster at the ISMB conference.

Sept 2010 – June 2013

BSc (Hons) in Genetics @ University of Edinburgh, UK

  • Achieved 1st class despite skipping the first year of study via direct entry to the second year.

Technical Skills

  • Programming languages: Python, SQL, R.

  • Traditional AI (Machine learning and statistics): Exploratory data analysis, classification / regression, statistical analysis, time-series forecasting, deep learning.

  • Modern AI (Generative and agentic AI): OpenAI Agents SDK, Deep Agents, LangGraph, Llama.cpp, Hermes Agent, Google Antigravity, GitHub Copilot.

  • Machine learning libraries: scikit-learn, Nixtla, Hugging Face and PyTorch.

  • Monitoring and observability (MLOps): MLflow, AWS CloudWatch, Prometheus/Loki/Grafana.

  • Data visualisation and dashboarding: seaborn, dash, Tableau and Power BI.

  • Data processing libraries: Python (Pandas, Polars), Apache Spark.

  • Cloud services: AWS, Databricks, DigitalOcean – Compute instances (e.g. ECS, Kubernetes), storages (e.g. S3, Postgres), networking (e.g. load balancing), serverless computing (e.g. Lambda).

  • Other technologies used: Docker, Git, Agile, Apache Airflow, Terraform, SAP S/4HANA, Domino Data Lab.

Publications

  • Distinct molecular trajectories converge to induce naive pluripotency. Cell Stem Cell September 2019.

  • Understanding transcriptional regulation through computational analysis of single-cell transcriptomics. Doctoral thesis September 2017.

  • BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics September 2016.

Certifications

  • AWS Certified Solutions Architect - Associate

  • Passed CFA level 1

Languages

  • English – fluent

  • Mandarin – native

Interests

  • Functional training – Enjoy the physical challenge and sense of achievements when I can be better than my former self.

  • Website building – Enjoy the mental challenge of building and deploying cloud services, which is like assembling Lego bricks in the digital space.