Van (Ritsuka) Ting
Jersey City,NJ
Summary
Data scientist with a strong background in machine learning, statistical analysis, and predictive modeling. Experienced in working with complex datasets and extracting insights to support evidence-based conclusions. Proficient in Python, R, SQL, and data visualization tools, with a focus on clear communication of analytical results. Previous work has a strong focus on developing data-driven approaches that efficiently deliver meaningful insights.
Overview
2
2
years of professional experience
Work History
Data Science Intern
Fitch Labs (NYU)
10.2023 - Current
- Responsible for the application of software and statistical solutions to multiple in-progress research projects.
- Currently engaged in a project that utilizes k-means and random forest clustering methods to propose gene interactions
- Responsible for developing and overseeing data cleaning and processing pipelines that are written in both R and Python
- Responsible for precise documentation of all programming projects
Data Science Intern
Comediq
New York, New York
06.2025 - 08.2025
- Created transcription and analysis pipelines, which utilize heuristics and sentiment analysis to determine how audiences receive a host's performance
- Evaluated model performance metrics such as accuracy, precision, recall, and F1 score.
- Researched and created possible implementations for various modes of audio transcription and analysis
Data Science Intern
Climate Cognition Lab (NYU/Stanford)
10.2024 - 05.2025
- Created and administered polls, forums, and other methods for gauging public opinion.
- Conducted data cleaning, analysis, and interpretation using visualization software like Tableau and ggplot2.
- Performed audio-to-text transcription followed by analysis through heuristics and sentiment evaluation.
- Led a personalized research project focusing on public opinion regarding federal legislation.
Education
Bachelor of Arts Joint Degree - Computer Science, Data Science
New York University, College of Arts & Science
New York, NY05.2026
Skills
- Machine Learning Libraries (Scikit, Keras, Pytorch)
- Machine Learning Basics and Statistical Modeling (Linear Regression, Naive Bayes, Decision Trees, K-Nearest Neighbors (KNN), Principle Component Analysis (PCA), Hyper-Parameter Tuning)
- Clustering and Unsupervised Learning (K-Means, K-Prototypes, DBSCAN, Hierarchical Clustering)
- Ensemble Methods (Random Forrest, Gradient Boosting, Bagging)
- Deep Learning and Neural Networks (Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Recurrent Convolutional Neural Networks (RCNN))
- Data Visualization Libraries (Tableau, Plotly, Matplotlib, ggplot2)
- Various Programming Languages (C, HTML, Java, JavaScript, Python, R)
Accomplishments
- International C. Elegans Conference: Presented and verified my statistical models to various experts from across the world. Facilitated new research connections that resulted in an overall increase in lab resources and funding
- NYU Undergraduate Research Conference: Presented end-of-year research results in a university-wide exposition towards professors, students, and prospective research partners for Fitch Labs
Academic Awards
NYU: Deans Undergraduate Research Fund 2025-2026
Timeline
Data Science Intern
Comediq
06.2025 - 08.2025
Data Science Intern
Climate Cognition Lab (NYU/Stanford)
10.2024 - 05.2025
Data Science Intern
Fitch Labs (NYU)
10.2023 - Current
Bachelor of Arts Joint Degree - Computer Science, Data Science
New York University, College of Arts & Science