Liu Jason Tan
Quantitative Risk Analyst at Morgan Stanley
August 2022 - Present
Developed end-to-end risk models using Python to automate manual calculations. Optimized NLP and machine learning models to improve risk incident quality assurance. Collaborated with global teams to achieve strategic goals, mitigated risk, and mentored junior team members while delivering high-quality work ahead of deadlines.
June 2021 – August 2021
Applied web scraping and API integration to collect and analyze public data, providing actionable insights that led to product UI improvements. Conducted customer interviews and qualitative research to identify critical insights, contributing to enhancements in customer satisfaction and driving key management decisions.
October 2017 - May 2020
Provided technical support to end-users, both remotely and on-site, translating complex issues for non-technical users. Performed advanced hardware diagnostics to ensure device security and prevent vulnerabilities. Managed communications and escalated issues to appropriate departments, ensuring prompt resolution and maintaining operational efficiency.
Developed a comprehensive dashboard for real-time sentiment and topic monitoring of company discussions. Utilized supervised and unsupervised learning techniques, including BERT for emotion classification (e.g., surprise, anger, disgust) and non-negative matrix factorization for topic clustering (e.g., account issues, ordering issues, service issues), to gain actionable insights from social media interactions.
Achieved 62% precision with a random forest classifier, a substantial improvement over the 20% precision of a dummy classifier. Successfully categorized stocks into top, middle, and bottom tiers using key equity metrics such as price-to-earnings ratio, dividend yield, and volatility.
Conducted a comprehensive analysis of S&P 500 stocks using k-means, agglomerative clustering, and affinity propagation to identify optimal stock groupings. Evaluated clustering methods based on Calinski-Harabasz, Davies-Bouldin, and Silhouette scores, leading to a refined unsupervised clustering model that revealed key sectoral insights.
Received first place by leveraging NLP techniques to analyze sentiment in text message surveys regarding COVID-19. Automated data cleaning, text encoding, and hierarchical clustering using BERT to improve the efficiency of research and generate deeper insights.
Performed predictive analysis on call data to optimize client contact frequency using a Multi-Layer Perceptron neural network. Employed GridSearchCV for hyperparameter tuning and conducted detailed model interpretation and evaluation.
Conducted comprehensive data analysis on Tesla vehicle performance, evaluating efficiency in relation to temperature, average speed, and driving smoothness. Utilized Python for data preprocessing, analysis, and visualization, and tracked battery degradation over time to identify patterns and trends.
