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Principal Data Scientist Resume

Newark, CA


Experience: 5+ years unsupervised machine learning, data mining, and big data research. 5+ years in economic analysis and large scale data analytics.


Programming Languages: R, Python, SAS, SQL, MATLAB, STATA, C, Shell

Computing Framework: Hadoop/ MapReduce

Operating Systems: Windows, IOS, Linus, Microsoft Series, Latex, Adobe Illustrator



Principal Data Scientist


  • Initiated and planned the scope of data scientist work in the company with co - Founders.
  • Designed and developed algorithm end-to-end to support the business needs, such as built predictive model based on eye-movement video to predict the probability for various diseases.

Confidential, Newark, CA

Ph.D. Research Intern

  • Proposed a bootstrapping algorithm in R to measure the standard error of property damages estimation through limited sample size.
  • Researched and developed an innovative modeling algorithm to quantify insurance risk in R.
  • Designed, tested, and implemented algorithm into the Confidential Assessment Product for the finance team to accurately and efficiently create premiums.
  • Re-wrote the existing algorithm that quantifies property damages of 10GB+ of data.
  • Reduced computing time significantly and yielded costs savings of 75%.
  • Drove valuable insights for the Vulnerability Team that reflect the latest data regarding disasters and risk.
  • Insights were featured in various Confidential models.


Confidential, Davis, CA


  • Proposed a novel data-driven protocol to understand the Malbec wines structure in terms of biochemical and sensory features: (1) Identified synergistic features as variable selection for classifying wines with 20% improvement. (2) Organized a collection of knowledge loci and further for prediction.

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