I understand data science end to end from automated data collection and processing to report creation and deep dives. I gained that perspective by having made substantial contribution in research, product management, software development and marketing teams. I enjoy analyzing data for actionable insights and building tools and features that allow people to leverage those insights and
I understand data science end to end from automated data collection and processing to report creation and deep dives. I gained that perspective by having made substantial contribution in research, product management, software development and marketing teams. I enjoy analyzing data for actionable insights and building tools and features that allow people to leverage those insights and collect even more in-depth data.
I appreciate thinking out of the box, always listen, and I don't mind being challenged. Dealing with uncertainty and vagueness is my specialty because that's what statisticians do. Only work that will make a meaningful impact will get me most excited. That's a good thing because purpose and passion are what drive me forward.
Data Scientist @ At Azure Cloud:
- Spearheading sentiment analysis from social signals (Twitter etc.) for quantifying the impact of favorable and unfavorable events related to our business. Prototyping dashboards and Word2Vec-based models.
At MSN and MSN Apps:
- I was an architect and a key contributors for building out end to end a brand new telemetry infrastructure for the new MSN and MSN apps across all major platforms (iOS, Android, Windows). I helped introduce a framework from a statistical point of view and made sure the system is designed for fulfilling analysis needs instead of being just a piece of software engineering. I generalized user action on different devices and came up with clear descriptions of various elements on an UI to be captured.
- I was also responsible for building out real-time reporting using Omniture for the new MSN and MSN Apps. In the process I have gained tremendous understanding in the dimensions and segments needed in the tool to identify opportunities for growing our business.
- To ensure the data that we are getting from the new MSN and MSN Apps are trustworthy, I have independently built a fully-automated pipeline for monitoring and alerting anomalies in the data.
- I have independently spec'd, pm'd and tested a separate video telemetry and reporting pipeline for the new MSN and MSN Apps to capture users' watching behavior in order to drive better engagement and more accurate targeting for advertising.
- Before the new video telemetry and reporting was built, I had been the maintainer of the legacy system that submits scripts for MapReduce processing, imports processed data into SQL DB and generates cubes and dashboards. I have learned the ins and outs of such a system and applied my learning to the design and implementation of the new system. From January 2014 to Present (2 years) Greater Seattle AreaMarketplace Manager, Revenue @ At Bing Ads R&D:
- I have applied Generalized Mixed Models to model positional factors on the new Bing UI, accounting for the effect of annotations. The extracted features (predictors) were than used in fitting logistic regression models in the MapReduce cluster. The outcome of the study helped greenlight the change that started Bing's steady climb in market share since 2013.
- I have lead and syndicated the most comprehensive user engagement impact study ever in Bing Ads by comparing the distribution of ad quality across all keyword segments. It helped bring focus to addressable issues instead of being constantly distracted by noise. It also shifted the analysis paradigm from per-ad to per-user basis.
- To identify gaps in ads quality among leading search engines, I have conducted a competitve ad relevance analysis to find out custom quality thresholds for different market segments, using human judgment data fitted with Random Forest and SVM. It lead to the implementation of adaptive thresholding features that helped significantly improve the whole-page relevance across all segments, while holding RPS constant or better.
At Search Business Group:
- I have conducted advertiser demand analysis via large-scale auction simulations and identified categories of industries to drive for sales for the highest ROI. The conclusions lead to a focused approach for the field sales team that resulted in an RPM bump.
- In order to achieve win-win with our publishing partners, I have carried out threshold tuning and optimization for throttling qualified ad inventory that helped achieve a great balance between revenue and relevance.
- I have made use of the methodology developed in my doctorate dissertation to model and understand the effectiveness of the four major marketing initiatives targeting premium advertisers at the time. It resulted in the identification of the only effective initiative and helped save money and resources worth hundreds of thousands of dollars. From July 2011 to December 2013 (2 years 6 months) Greater Seattle AreaLead TA for Business Stats Class @ - Assisted in designing course material, providing team leadership, supervising 6~8 other TAs. From January 2008 to December 2010 (3 years) ORISE Fellow @ - Performed data quality and risk assessment, meta analysis on drug safety data on 10+ data sets of on average 1.5 million records each.
- Applied innovative graphical methods for pattern identification. From June 2010 to August 2010 (3 months) Statlab Consultant @ - Assisted clients from on and off campus for problems in all statistical areas. From April 2008 to June 2010 (2 years 3 months) Summer Intern (PhD level) @ - Built predictive models, performed risk analysis of health plan customers.
- Applied machine learning techniques to 7 million customer visiting records. From June 2009 to August 2009 (3 months) Teaching Associate @ - Taught Applied Statistics course for social science and biology majors.
- Independently developed lecture notes, homeworks, quizzes, exams; supervised one TA. From July 2007 to August 2007 (2 months) Engineering Intern @ - Assisted technology development of next generation high performance Atomic Force Microscopes.
- Performed pilot process testing and diagnostics, yield improvement data analysis.
- Assisted design of capacitive sensor for dielectric characterization, experimental data analysis.
- Modeled tip-sample interaction under capillary force in tapping-mode AFM. From April 2004 to November 2005 (1 year 8 months) Lab Engineer @ - Designed and fabricated a novel AC electrokinetic BioMEMS micropump.
- Performed digital image processing (based on maximum likelihood) for the study of microflows. From October 2003 to March 2004 (6 months)
PhD, Statistics and Applied Probability @ University of California, Santa BarbaraMS, Mechanical Engineering @ Purdue UniversityBE, Engineering @ Tsinghua University J Wu is skilled in: R, Statistics, Data Mining, Data Analysis, Predictive Modeling, Time Series Analysis, Analytics, Matlab, SAS, SQL, Survival Analysis, Business Intelligence, Monte Carlo Simulation, Data Visualization, Python