Emma Ozanich, PhD

Industry and data scientist with 8 years development and 3 years leadership experience using statistics and machine learning in Python and R.

Education

Ph.D. Acoustical Oceanography University of California, San Diego 2020
M.S. Acoustical Oceanography University of California, San Diego 2017
B.S. Physics Hamline University, St. Paul, Minnesota 2014


Experience

JASCO Applied Sciences Project Scientist, Remote (Denver, CO) 2022 - Present
  • Lead data analysis development of predictive models and visualizations for environmental data, using Sourcetree/Bitbucket version control
    • Merge and summarize data tables to visualize key outcomes with R data table, ggplot
    • Model linear, nonlinear regression and statistical percentiles for timeseries data
  • Communicate outcomes and collaborate with external stakeholders to align data science objectives with business goals
    • Serve as technical expert in meetings with cross functional industry and government stakeholders
    • Author technical memos and reports for publication in public documents
  • Create software routines to improve data interpretation and efficiency
    • Developed data reporting tools for accelerating large report generation
    • Created interactive Python Panel dashboard for visualizing datasets with seaborn, matplotlib
Woods Hole Oceanographic Institution Postdoctoral Investigator, Woods Hole, MA 2021 - 2022
  • Modeled and analyzed ocean timeseries data in MATLAB
    • Used large-scale, GPU-accelerated predictive modeling to investigate environmental questions
    • Cleaned and validated data to remove outliers, sub-sampled and filtered raw timeseries to select signals of interest
University of California San Diego Graduate Researcher, San Diego, CA 2016 - 2020
  • Designed machine learning models for acoustic sensing in Python Keras and Tensorflow to improve predictive accuracy
    • Used deep learning to estimate source angle with over 90% accuracy, robust to random noise
    • Developed a deep clustering model for coral reef sound datasets using convolutional auto-encoder in Keras and Scikit-Learn
  • Presented research results at semi-annual conferences and published in reputable acoustics journal
Graduate T.A. Electrical and Computer Engineering (ECE 228) 2019, 2020
Research Mentor Haliçioglu Data Science Institute 2018
  • Created and presented Jupyter Notebook demo analyzing BiqQuery timeseries data with SARIMA

Skills

  • Languages: R, Python/Jupyter Notebook, IDL, pSQL, linux/unix, MATLAB

  • Development: Sourcetree, Bitbucket, git CLI, familiar with Databricks

  • Reporting: Microsoft Word and Excel (VBA), LaTex, ArcGIS/QGIS