Yen Lok
Verified Expert in Engineering
Data Scientist and Machine Learning Developer
Kuala Lumpur Federal Territory of Kuala Lumpur, Malaysia
Toptal member since June 30, 2022
Yen is a data scientist, a Python enthusiast, and a machine learning (ML) developer. He has been a data scientist in several large multinational companies. Yen has developed data pipelines and machine learning (ML) models. His unique combination of domain knowledge in business, science, and technical skills allows him to provide his clients with actionable, predictive, and explanatory models.
Portfolio
Experience
- Machine Learning - 7 years
- Python - 7 years
- Data Science - 6 years
- Time Series Analysis - 5 years
- Data Analysis - 4 years
- Data Modeling - 4 years
- Financial Markets - 3 years
- Data Analytics - 3 years
Preferred Environment
PyCharm, Slack, Python 3, SQL, Google Cloud Platform (GCP), Machine Learning, Artificial Intelligence (AI), Analytics, Time Series Analysis, Predictive Modeling
The most amazing...
...thing I've developed is a sales propensity model for campaign ad-targeting, achieving an uplift of over four times.
Work Experience
Independent Data Science Consultant
Freelance
- Built streaming data pipelines to index blockchain smart contracts via Pub/Sub and Apache Beam.
- Optimized and trained DL models such as LSTM and Time2Vec transformer models to make time-series predictions for a ski tracking app.
- Engineered features and trained ML models for multiple use cases, such as predicting the churn rate for an insurance product, the likelihood of an employee changing jobs within a year, etc.
- Built and deployed a dynamic pricing model for an eCommerce product using GA data.
- Designed and automated a dashboard in Google Data Studio, including data preparation automation in BigQuery/Python and web scraping for external data sources.
- Deployed content generation APIs using the OpenAI GPT3 model and text-to-image models, e.g., the DALL·E model.
Data Scientist
AirAsia Group Berhad
- Developed an automatic machine learning (ML) propensity model for marketing ad-targeting.
- Applied time series analytics to determine the department’s budget allocation.
- Built fraud detection system to identify account take-over fraud.
- Constructed a central feature store in BigQuery for predictive modelling.
- Built an app banner recommendation model to improve banner click-through rate.
Machine Learning (ML) Engineer
Neural Technologies
- Utilized a mix of supervised, unsupervised, and categorical modeling techniques to deliver the best predictive performance.
- Built a full suite of customized plots to analyze and interpret model outcomes.
- Set up the application of full software development life cycles. Included code reviews, version management, build model, and testing.
Data Scientist
AIA Group
- Performed a deep-dive analysis on company vitality using pseudo control matching to identify actionable insights.
- Managed profile vitality member behavior using graph sequencing and segmentation techniques to improve vitality member engagement rate.
- Developed reimbursement claims and a straight-through process using predictive analytics to improve claims settlement turn-around time.
- Built an anomaly detection model to identify outlier claims to aid the team in devising strategies to reduce overall claim costs.
Data Scientist
Logistic Worldwide Express
- Extracted, manipulated, and interpreted data from different data sources, such as the local postal service address database and OpenStreetMap data, to create data pipelines for building dashboards.
- Developed Power BI dashboards to assist the management team in tracking business performances and making data-driven decisions.
- Applied graph algorithms using Neo4j to optimize shipment routing, with an estimated delivery time savings of 10% to 20%.
Post-doctoral Researcher in Time Series ML
Aberdeen Standard Investments
- Applied time-series modeling and ML techniques to improve the multi-asset investment decision process.
- Built a backtesting framework for ML using MATLAB.
- Developed a novel approach to select predictive models.
Experience
Auto Machine Learning (ML) Propensity Model
• A feature store to cater to the data requirements of this and many other predictive models.
• A model validation framework allows us to track past and ongoing model performance.
• A scheduled model re-training to reduce the effects of data drift.
• Model deployment via Dataflow in Google Cloud Platform (GCP).
Banner Recommender System
Fraud Detection System
Education
Ph.D. Degree in Actuarial Mathematics
Heriot-Watt University - Edinburgh, United Kingdom
Master's Degree in Actuarial Science
Heriot-Watt University - Edinburgh, United Kingdom
Bachelor's Degree in Actuarial Science
University of Kent - Canterbury, United Kingdom
Skills
Libraries/APIs
Pandas, NumPy, Scikit-learn, Natural Language Toolkit (NLTK), SpaCy, REST APIs, TensorFlow, Keras, PyTorch, Matplotlib
Tools
BigQuery, Microsoft Power BI, PyCharm, MATLAB, Apache Beam, Excel 2013, Amazon SageMaker, GIS, AWS Glue
Languages
Python 3, SQL, Python, R
Paradigms
Automation, ETL
Platforms
Jupyter Notebook, Google Cloud Platform (GCP), RStudio, Docker, Amazon Web Services (AWS)
Storage
Data Pipelines, Google Cloud, Neo4j
Frameworks
Flask
Industry Expertise
Marketing
Other
Machine Learning, Artificial Intelligence (AI), Time Series Analysis, Predictive Modeling, Data Analytics, Data Science, Data Engineering, Data Warehousing, Data Architecture, ETL Tools, Predictive Analytics, Google BigQuery, Regression Modeling, Gradient Boosting, Linear Regression, Logistic Regression, Random Forest Regression, A/B Testing, Data Processing, Decision Trees, Statistics, Financial Modeling, Mathematics, Research, Financial Markets, Deep Learning, Big Data, Recommendation Systems, Natural Language Processing (NLP), Data Scraping, Data Visualization, Infrastructure as Code (IaC), Dashboards, Quantitative Analysis, Quantitative Modeling, Data Analysis, Data Modeling, Financial Forecasting, Risk Analysis, Statistical Modeling, Generative Pre-trained Transformers (GPT), Computational Finance, Large Language Models (LLMs), Risk Models, Enterprise Risk Management (ERM), Stochastic Modeling, Probability Theory, Bayesian Inference & Modeling, APIs, Algorithmic Trading, Algorithms, Kalman Filtering, Explainable Artificial Intelligence (XAI), Quantitative Finance, Quantitative Risk Analysis, Analytics, GRAPH, Image Generation
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