- Led comprehensive data science and design initiatives, developing predictive models, validation strategies, and design methodologies to enhance algorithm accuracy and product consistency across consumer segments- Implemented advanced data processing and preprocessing techniques to prepare complex datasets for machine learning model development, statistical analysis, and data-driven product design- Engineered systematic ML model validation frameworks using robust statistical techniques to assess prediction performance, reliability, and alignment with research and consumer objectives- Created comprehensive data visualization and reporting tools to effectively communicate technical insights to both technical and non-technical stakeholders, facilitating clear understanding of complex analytical findings- Spearheaded component library development and design tool creation, leveraging atomic design principles to streamline product development processes and improve efficiency across design and data science workflows

• Advising multiple Pittsburgh area high school teams through generatingresearch questions, collecting data, and navigating data science softwareprograms.• Leading weekly mentoring sessions to direct students towards usefulresources and assisting in the resolution of issues throughout the research process.

• Designed and developed a GitHub repository to teach nationwide highschool DataJam contestants how to implement data analysis methodsusing Python and collaborate with GitHub.• Resources delivered include techniques for data issues management,data normalization and statistical testing approaches.