People / Students / Class of 2026
Xingye (Athena) Chen is a data science professional with a strong foundation in business and analytics, currently pursuing her Master of Science in Machine Learning and Data Science at Northwestern University. She graduated Magna Cum Laude from New York University’s Leonard N. Stern School of Business with concentrations in Finance and Computing & Data Science.
Having spent four years of undergraduate study in one of the greatest cities in the world and gained hands-on experience across various industries, Xingye has developed a nuanced understanding of how the business world perceives technological change and how the rapidly evolving technology ecosystem bridges finance and innovation on a global scale. Her experience has equipped her with skills in both qualitative and quantitative analysis, enabling her to effectively connect business insights with data-driven decision-making.
In her professional life, Xingye has been dedicated to applying her interdisciplinary knowledge in practice. As a Data Science Intern at NextTier, a technology consulting firm, she designed A/B testing frameworks and K-means segmentation models that increased CTR by 8% and supported marketing strategy with scalable Tableau dashboards. At TK Elevator, she led end-to-end exploratory data analysis on more than 30,000 contracts, engineered predictive models using XGBoost and clustering, and helped reduce predicted churn by 20%, driving a projected $10 million annual impact. By combining advanced analytical methods with a clear understanding of business objectives, she effectively translates data insights into actionable strategies that drive measurable growth.
She also has experience optimizing user interaction and enhancing app engagement. Using SQL and Python, she analyzed structured behavioral data and built regression models to inform UI/UX redesigns, resulting in a 15% increase in click-through rates. She led a credit card fraud detection project, improving model accuracy on imbalanced data using techniques like undersampling and SMOTE, and developed and tested multiple classifiers—including Logistic Regression, Random Forest, XGBoost, and Neural Networks—that achieved 93% precision in fraud identification. Her interdisciplinary understanding ensures that technical solutions are aligned with business needs, enabling more effective decision-making and risk management.
As she begins her academic journey in the MS in Machine Learning and Data Science program at Northwestern University, Xingye aspires to leverage data science to solve complex business problems, combining technical rigor with strategic insight to drive meaningful, data-informed decision-making. Embracing the mindset cultivated at Northwestern, she is confident in her ability to grow into a thoughtful and responsible leader by driving innovation and advancing the future of finance and technology integration.
