Maggie Shao

Data Science & Cognitive Science Student

San Diego, CA · yis082@ucsd.edu

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Education

University of California, San Diego

La Jolla, CA · –

  • B.S. in Data Science
  • B.S. in Cognitive Science, Machine Learning Specialization
  • GPA: 3.83

Experience

Data Scientist Intern — Applied Materials

Santa Clara, CA · –

  • Built an automated Python/SQL monitoring pipeline that ran scheduled scripts across 856 contracts, identifying 18 at-risk cases for proactive follow-up and earlier issue detection.
  • Consolidated fragmented reporting workflows into a centralized Tableau dashboard supporting 280 active cases and adopted by 20+ cross-functional stakeholders.
  • Partnered with product, operations, and product-line teams to translate business requirements into issue-tracking logic, reducing contract review time by 50%.

Data Science Intern — IDX Exchange

Remote · –

  • Built an end-to-end machine learning workflow for 10K+ property records, applying KNN imputation and feature standardization to prepare structured data for model training.
  • Trained and evaluated Random Forest and Gradient Boosting regression models, improving property price prediction accuracy by 12%.
  • Developed interactive Python/Plotly visualizations to translate regional pricing patterns into actionable market insights for stakeholders.

Technical Support Intern — Cisco Global Support Center

Remote · –

  • Analyzed 7K+ system logs to identify recurring issue patterns and translated findings into Excel dashboards supporting operational decision-making and process improvement.
  • Developed Python automation scripts to streamline troubleshooting workflows, reducing average issue resolution time by 25%.
  • Resolved 50+ end-user technical issues within SLA requirements and collaborated with engineering teams on escalations and root-cause investigation.

Projects

California Real Estate Market Analysis

Python · Pandas · scikit-learn

  • Processed 178,925 California real estate listings through a reproducible ML pipeline, performing data cleaning, one-hot encoding, and feature scaling for price prediction.
  • Trained and evaluated a scikit-learn Linear Regression model, achieving R2 = 0.77 on an 80/20 train-test split.
  • Performed residual and prediction-error diagnostics, identifying higher variance among high-priced properties to guide model refinement.

Explore my projects →

Technical Skills

Leadership and Affiliations