Chee Yee Lim

Data Scientist in Singapore

"The most important and difficult aspects of data science are often not on the machine learning techniques, but on defining clear business problems and engaging stakeholders throughout the process."

Full stack data scientist with a focus on solving actual business problems using the most effective machine learning techniques.

Experienced in the full data product lifecycle from data collection, through processing and analysis, to production deployment and communication of results to clients.


A selection of dashboards built using Python Dash and public data, with a sprinkle of machine learning.

Singapore Resale HDB Analytics

Dashboard to understand the pricing history of HDB resale flats.

Open Dashboard
NLP-powered Text Analyser

Tool to analyse and extract information from any input text.

Open Tool
Optimisation Visualiser

Dashboard to visualise algorithms searching on optimisation surface.

Open Dashboard


A selection of articles on everything data science.

Blog Posts

Blog posts to share my data science/machine learning journey

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Code Templates

Code templates that I have written to handle common data science/machine learning tasks

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Senior Data Scientist

DHL Consulting, Singapore
  • Proposed data solutions that align business and technical requirements while making sure that they can be delivered with available resources and timelines.
  • Communicated complex technical topics to business audiences using analogies to obtain buy-ins, which result in stronger confidence and engagement with our data capability.
  • Resulted in 5 data science projects being delivered on time, bringing in a total revenue of $430,000 and achieving an average customer satisfaction Net Promoter Score of 50.0.
  • An example project developed is a Natural Language Processing (NLP) data solution that highlights trending topics and associated KPIs (e.g. sentiments) from automatically ingested chats, emails and calls data received from customers.
  • The NLP data solution provides visibility into incoming customer queries for the first time to our client, resulting in shorter response time, lower staffing cost and improved service quality.
  • Managed a team of 4 data scientists both as a team lead and a project manager with a focus on empowering them to grow professionally and deliver quality projects on time.
Sept 2020 - Present

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.
  • Deployed the random forest-based model into production in 2 forms: (1) as a dockerised Flask API model for generating real-time predictions on new data, and (2) as a PySpark pipeline for generating batch predictions on historical data.
  • Worked closely with a team of 3 product managers and 2 engineers to ensure the product is developed on-time while achieving business goals (i.e. user-requested features) and fitting into existing IT infrastructure (i.e. data ETL pipelines).
  • The patent value product replaced a third-party patent value data provider, which helps our company saves $100,000 per year in subscription fee.
July 2019 - Sept 2020

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.
  • Engineered the backend ETL using distributed processing (Scala + Spark + SQL) and the frontend using R Shiny dashboard (Rmarkdown + plotly).
  • 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 investment 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.
September 2017 - May 2019

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.
  • Resulted in 2 research papers, one of which is a first-authored paper.
  • Commended by at least 3 senior researchers on my public speaking ability in presenting complex technical terms clearly and passionately.
October 2013 - September 2017


University of Cambridge, UK

PhD in Computational Biology
  • Graduated on-time with 2 research papers and presented a poster at the ISMB conference.
  • Tutored for 2 bioinformatics courses and led the Wolfson College Table Tennis team.
October 2013 - September 2017

University of Edinburgh, UK

BSc (Hons) in Genetics
  • Achieved 1st class despite skipping the first year of study via direct entry to the second year.
  • Represented 30 students as a class representative and voiced out concerns affecting students.
September 2010 - June 2013


Programming Languages & Tools
  • Python
  • R
  • SQL
  • HTML
  • Apache Spark
  • Apache Solr
  • PyTorch
  • Git
  • Docker
  • Cloud Computing
Machine Learning & Statistical Methods
  • Exploratory Data Analysis
  • Natural Language Processing
  • Time-series Data Analysis
  • Traditional Machine Learning
Human Languages
  • English - Fluent
  • Mandarin - Native
  • Malay - Intermediate
  • Cantonese - Conversational


  • Google Cloud Trainings ( View)
  • Passed CFA level 1
  • Investment Management Certificate
  • Distinct molecular trajectories converge to induce naive pluripotency. Cell Stem Cell September 2019. ( View)
  • Understanding transcriptional regulation through computational analysis of single-cell transcriptomics. Doctoral thesis September 2017. ( View)
  • BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics September 2016. ( View)


  • Table tennis

    Enjoy the intense focus required and rapid adaptability nature of table tennis in competitions.

  • Meetup events

    Attend Meetup events regularly to keep up with the latest development, and to network with people from different backgrounds.

  • Travel

    Enjoy hiking in nature and experiencing different culture in a foreign country.