• Observability Logging Team

• Led a seismic station quality control team of 2 senior data analysts in aggregating and analyzing two years of continuous seismic time series using Python and diagnosed 26% of stations are under-performing based on statistical analysis. Performed technical report as a reference to Global Seismographic Network, and expected increase 20% data availability by correcting suggested discrepancy.• Wrote a Python web crawler to retrieving earthquake data from FDSN data center with given conditions andprocessed 50GB unstructured noisy data to a structured data set.• Utilized big data (more than 5.2 million data point) to build numerical Earth model (tomography), resulting ina 20% increase in variance reduction when comparing predicted seismic time series to the observed data.

• Built numerical model to predict theoretical wave time series with lowering misfit by 20% compared to the initial model using C and Python.• Submitted official research report and gave presentation in international conferences AGU.

• Classified specific seismic characteristics from 100+ seismograms dataset through Python and saved an average of 10 minutes per record (16+ hours saved).• Wrote the research paper and presented analysis insights to the senior project managers.