Shan Huang Email & Phone Number

Austin, Texas, US

Draper, UT, US

Serve as data science principal on multiple cross functional product teams, all of which are heavily dependent on data science and machine learning (adaptive skill assessment, recommendation engine, personalized learning experience, and learners’ insight).

Draper, UT, US

• Develop recommendation algorithms to deliver personalized product experience.
• Create prototypes for data science products and build interactive web apps to demonstrate algorithms.
• Develop and implement customer segmentation, retention, and lifetime models.
• Provide analytical solutions to business and strategic problems.

• Analyzed users’ listening behaviors, music preferences and mobile interactions using high volume music streaming data and web beacon to improve engagement, conversion and retention.
• Leveraged consumer survey and behavioral data to study the interplay of demographics, listening preference and product experience. Identified significant user segment and provided actionable insights on product design.
• Collaborated with Content team to evaluate ROI of in-house and partner stations. Applied cohort and cross-sectional analysis to measure impact of new released stations on a broad spectrum of company KPIs.
• Worked closely with IT and data warehouse team to understand data sources and limitations, discuss desired data structure in warehousing system, and design automated data processing for reproducible analytical report.

San Diego, CA, US

• Responsible for the full analytical cycle from requirement gathering to data assembly, model development, validation and implementation. Created and deployed eight predictive models related to student attrition and.
• Implemented predictive models in SQL server as a part of ETL project, designed automated model monitoring process to track production model performance, promoted and consulted the use of visualization tool to detect.
• Executed data extraction from large, complex data warehouse, applied extensive data mining techniques (classification, association rules, time series, etc) to provide in-depth analyses for business questions. Prepared.
• Processed and cleaned unstructured text data from course assignments, class discussion board and open-ended surveys, extracted underlying themes and topics from large volume of documents using text mining techniques..
• Participated constructively in a series of executive level ad hoc analyses. Leveraged various data sources to create performance metrics, identify trends, quantify the benefit of initiatives and develop strategic.

San Diego, CA, US

• Advanced research work of faculty investigator by effectively conducting literature review of professional publications and preparing analysis dataset.
• Implemented various statistical methodologies (GLM, Mixed effect, bootstrap and Monte Carlo simulation) by SAS and R programming in a timely manner, ensuring successful completion of projects.Research Project.
• Introduced the geometric process model into the analysis of accelerated life test. Derived maximum likelihood estimators and confidence intervals using Newton Iteration algorithm via R programming.
• Monte Carlo simulation shows significantly improved confidence interval coverage compared to the traditional model.

San Diego, CA, US

• Research Project: Survival Analysis of Pneumonia in Young Children
• Implemented survival analysis techniques including KM survival curves and Cox proportional hazards model to identify potential factors associate with pneumonia in young children.Research Project: Impact of Auditor.
• Assembled raw data; built and validated models to discover the impact of auditor industry expertise on bank loan loss provisions.

San Diego, California, US

• Actively exchanged and contributed ideas with Vice President and engineers of Quality Control to define, develop and evaluate project plan.
• Manipulated substantial reliability data obtained from lab experiments.
• Developed accelerated failure time model, resulting in a successful estimation of capacitor cells’ lifetime under regular use.

• Carried out a survey project for a food organization from start to finish. Detected barriers that prevent people from using their service and assessed the operational needs of the food programs.
• Collected, coded and analyzed survey data using statistical methods including descriptive analysis, contingency table analysis, and logistic regression.
• Developed and pretested telephone survey questionnaire, conducted four focus groups and interviewed 301 people, analyzed data and delivered actionable findings in six months with four colleagues.
• A brief research summary can be reached at http://www.chicagosfoodbank.org/site/DocServer/WorkingPoorStudy_8.09.pdf?docID=4881

Master'S Degree, Psychology

Ms, Statistics

Bs, Psychology