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 InternResponsibilities:
- 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.