About – Chee Yee Lim

Experience

July 2023 – Current

Data Science Manager @ Johnson & Johnson, Singapore

Led a team of 4 data scientists to deliver machine learning-based financial forecasts for 5 production projects, supporting the global enterprise finance team in their digital transformation efforts and facilitating more informed, data-driven decision-making.
Boosted adoptions of our sales forecasts in 4 key markets by engaging with digital transformation leads and business stakeholders to improve the efficiency of the business planning processes.
Communicated complex technical topics to business audiences using analogies to obtain buy-ins, which resulted in stronger confidence and engagement with our data capability.
Championed the digital learning and development needs of team members and the wider finance community by hosting a lunch and learn series, as well as speaking at digital engagement events.
One project example is the monthly sales forecasts produced using our proprietary Nixtla-based Python forecasting library, as part of the Integrated Business Planning process. This process covers 2 million country-SKU combinations globally, providing baseline forecasts that enable commercial, supply chain, and finance functions to better align their strategies.

June 2022 – June 2023

Data Science Manager @ DHL Consulting, Singapore

Sept 2020 – June 2022

Senior Data Scientist @ DHL Consulting, Singapore

Completed 8 data science projects (5 of which are in production) by leading a team of 3 data scientists to solve business problems for clients, ranging from customer retention to fraud detection.
Delivered an estimated total annual savings of SGD 860,000 while achieving an average customer satisfaction Net Promoter Score of 68.8 via the projects.
Partnered closely with business stakeholders to jointly define problem statements and align priorities, so that projects can be delivered on time and within budget while satisfying clients’ needs.
Led business development meetings and workshops with cross-divisional regional or country heads that brought in 5 data science projects, totalling SGD 570,000 in revenue.
One project example is the customer service analytics data product that highlights trending topics and associated KPIs (e.g. sentiments) from incoming customer chats, emails and calls using Natural Language Processing (NLP), resulting in shorter response time, lower staffing cost and improved service quality for our client.

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.

Oct 2013 – Sept 2017

PhD Researcher @ 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.
Reconstructed the development timeline with a polynomial model fitted to a kernel PCA-reduced space, which enables the subsequent inference of potential causal relationships among genes using penalised vector autoregression and Boolean models.
Commended by at least 3 senior researchers on my public speaking ability in presenting complex technical terms clearly and passionately.

Oct 2013 – Sept 2017

PhD in Computational Biology @ University of Cambridge, UK

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.

Programming languages: Python, SQL, R.
Machine learning and statistical methods: Exploratory data analysis, standard classification / regression, statistical analysis, time-series forecasting, and natural language processing.
Data processing & storage technology: Python (Pandas, Polars), Apache Airflow, Spark and relational databases (SAP, MS SQL, MySQL, Hive).
Machine learning and deep learning libraries: scikit-learn, Nixtla, spaCy, Hugging Face and PyTorch.
Data visualisation and dashboarding: seaborn, altair, plotly, dash, Tableau and Power BI.
Cloud services: Virtual machines, managed storages & databases (AWS, DigitalOcean and GCP).
Software development tools and frameworks: Linux, WSL, Docker, Git and Agile.

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.

Powerlifting – 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.