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An innovative data science leader with more than 15 years experience in machine learning and data/analytics solution development and implementation. An expert in strategic analytics for modeling development, information extraction, insights discovery, opportunity identification and efficiency optimization. Tech expertise covers new frontier deep learning applications in natural language processing and MMM/forecasting/multi-channel attribution/segmentation, various machine learning modeling and statistical methodology development and implementation in media/retail/health care industry. A doctor degree in Biostatistics from UCLA and Certificate from DeepLearning.AI.
Natural Language Processing/Text Summarization/Sentiment Analysis/Topic Modeling
Predictive Modeling Applications/Customer Analytics/Segmentation/Classification
Strategic Analytics Solution Development/ Opportunity Identification/Efficiency Optimization
Store operation/Customer experience Drivers Identification and Quantification
Sales/Web Visits/Store Visits Forecasting
Multi-Channel Attribution/Digital Marketing Analytics/MMM
Tableau Dashboard Development/ Web Application Development
Recommendation System Development
Experimental Design/Survey Analysis/Statistical Analysis and Inference
Machine Learning Algorithm Development & Application
Deep Learning Algorithm Application & Development
University of California at Los Angeles
PHD in Biostatistics
MS in Mathematics/Statistics
Principal Data Scientist
Inga Data, Inc.
2014 – present
Developed MMM model to identify and select key metrics for store operation team to monitor and track to improve customer experience, facilitating store ops’ strategic planning and goal setting.
Developed forecasting optimization process and optimized leads forecasting performance across different channels and different regions.
Developed strategic survey analysis process and uncover the insights to facilitate branding/messaging strategy development.
Implemented Markov Chains attribution model and provided media spending optimization recommendation to one of leading media companies.
Advised multiple companies on marketing data and analytics strategic planning, insights automation, predictive modeling build-up and implemented data-driven operation efficiency and marketing effectiveness optimization recommendations.
Directed AuroraGo development—- a web-based machine learning portal for customer insights and marketing analytics automation tailored for retail industry.
Developed an artificial neural network framework for biomarker development and evaluation to gene expression data and published the technical article.
Applied deep learning/computer vision algorithm to various classification projects, published the project notes on quantiai.blog.
Director of Analytical Consulting
2010 – 2013
Provided data-driven, strategical analytical consultation to many fortune 500 companies including Citibank and Walmart to maximize customer values, discovered opportunities to improve operation and marketing efficiency and realized cost-saving for the client companies in millions.
Directed strategic marketing analytics roadmap, predictive modeling development, implementation and optimization. Some important projects listed in the Projects and Experiences section.
Sr. Manager of Customer Analytics
2006 – 2008
Developed predictive statistical models including logistic regression and segmentation for large direct mail campaigns to identify the target customers and targeted offers.
Participated in the development of a product recommendation system and conducted customer basket analysis to personalize product recommendations.
Ross product Division/Abbott Laboratories
2002 – 2006
Designed, developed and validated clinical trials’ statistical methodology including nonparametric AUC comparison and mixed effect modeling.
1. Developed customer experience driver identification and quantification modeling process to select key drivers from myriads of metrics and optimize strategic planning and goal setting.
2. Developed a forecasting optimization process which is easy to implement, flexible to add in relevant regressors and achieve forecasting accuracy improvement in a quick turnaround time.
3. Modeled customer journey as Markov Chains process and implemented MK multi-channel attribution modeling to facilitate channel spending optimization recommendations.
4. Developed a strategic survey analysis process which demonstrated plus performance and insights discovery than traditional approach and facilitate branding strategy development.
5. Developed an artificial neural network framework for biomarker development using gene expression data and published the research paper.
6. Ranked 9th on the Deep Learning Competition Northeastern SMILE Lab - Recognizing Faces in the Wild.
7. Applied supervised cluster analysis and forecasting model to identify customer segmentation and predict their migration patterns and prioritized opportunities, made marketing ROI maximization recommendation to a national retail chain to redeploy resources against high value opportunities as well as free up resources from wasted annual spending in the amount of $6 million.
8. Developed an enterprise wide behavioral segmentation for all traceable households of one of largest retail chain. 11 unique dimensions of customer information were created. A behavioral based segmentation was created to describe shoppers with 6 distinct household types using those customer dimensions. The segmentation has been the basis for 1) improving ROI on targeting by linking segments to media targeting and optimize media budget mix 2) providing insights on new business opportunities by dissecting the segments by geography, value tier and product details.
9. Utilized data and analytics to identify the bottleneck of operation pipeline of a leading international English learning institution, made recommendation to optimize operation efficiency, proposed short term and long term plan to improve lead conversion rate by implementing lead segmentation and lead scoring which had both long-lasting financial and organization impact on the institution.
10. Applied nested decision tree and nested logistic regression to optimize the ROI of Citi small business card direct mail campaign. The benefits of nested design are 1) revealing contradictory relationship between response likelihood and credit-worthiness. 2) matrixing the two models allows for identification of the prospects with the highest propensity to be approved. The nested decision tree approach is simpler to implement and provides direct insights into the small business segments with the high approval rate. The nested logistic regression approach provides the greater improvement over campaign performance.
11. Participated in the development of a product recommendation system and conducted customer basket analysis to personalize product recommendations at Sears Holding.
12. Designed, developed and validated clinical trials’ statistical methodology including stratified comparison, nonparametric comparison and mixed effect modeling.
What kind of projects is QUANTI AI looking for?
Data Science, Machine Learning, Analytics, Predictive Modeling, Natural Language Processing