Varshaa Jerry
New York,NY
Overview
7
7
years of professional experience
Work History
Data Scientist
Scripps Research Dept of Immunology
La Jolla, CA
02.2018 - 08.2024
- Evaluated and organized quantitative research data in SPSS, utilizing an analytical approach to ensure data integrity and timely resolution of discrepancies for accuracy of reporting
- Ran R scripts in Python to analyze data trends and manipulated data to predict outcomes
- Applied principal component analysis, clustering, and classification to determine trends in tumor growth
- Conducted many regression tests to make predictive analysis reports with considerable assumptions
- Communicated results by turning statistical analysis into graphs and charts for summaries of each trial for concise understanding and research study publications.
Data Science Intern
Halicioglu Data Science Institute
La Jolla, CA
01.2024 - 07.2024
- Contributed to research study that addresses causality detection in the presence of distributional shifts, hidden confounders, selection bias, nonlinear causal mechanisms, measurement error and missing values
- Employed statistical methods to clean and prepare data, enhancing the quality of information available to other research
- Analyzed over 1TB of datasets to identify key trends and patterns and helped create algorithms for various fields such as neuroscience, biology, and healthcare
- Successfully applied algorithms in major research projects throughout UCSD.
Education
Master of Science - Data Science
Columbia University
New York, NY12.2025
Bachelor of Science - Public Health And Biostatistics
University of California San Diego
San Diego, CA06.2020
Skills
- Machine Learning
- Analyzing & Synthesizing Data
- Quantitative/Qualitative Analysis
- Technical Communication
- R
- Python
- Data Visualization
- Statistical Analysis/Modeling
- Big Data Manipulation
Projects
Detect Credit Card Fraud Using Machine Learning
- Used card transactions dataset from Kaggle to classify credit card transactions into fraudulent and genuine
- Utilized R with algorithms such as Decision Trees, Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier to determine results
Stock Market Portfolio Optimization
- Analyzed price trends, calculated expected returns and volatilities, and determined the correlations between different stocks to achieve diversification
- Utilized Modern Portfolio Theory to create an efficient portfolio that relies on the efficient frontier to represent the optimal trade-off between risk and return
- Identified the portfolio with the highest Sharpe ratio, which indicated the best risk-adjusted return to achieve long-term investment goals
Spotify Recommendation System using Python
Timeline
Data Science Intern
Halicioglu Data Science Institute
01.2024 - 07.2024
Data Scientist
Scripps Research Dept of Immunology
02.2018 - 08.2024
Master of Science - Data Science
Columbia University
Bachelor of Science - Public Health And Biostatistics
University of California San Diego
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