New York, New York, United States
I work with data to build statistical models and extract insight from complex, real-world datasets. My background is in experimental research, where I developed reproducible analysis workflows, validated model performance, and evaluated uncertainty. I have hands-on experience with Python based data analysis, which includes NumPy, pandas, and SciPy. My work includes frequentist and Bayesian modeling techniques, hypothesis testing, and applying supervised machine learning tools to real-world data. I have applied these tools to a variety of tasks ranging from exploratory analysis to developing robust data pipelines that produce polished conclusions for clear communication. I’m currently seeking data scientist, data analyst, or applied data roles where I can use these skills to solve impactful, real-world problems. I’m open to opportunities across industries, and am especially motivated by work with social or public impact, including applications in healthcare, mission-driven organizations, or quantum information.
● Developed end-to-end analysis pipelines beginning with data cleaning and ending with Bayesian and maximum likelihood modeling to extract key insights from experimental data ● Built data-driven machine learning tools leading to a 5% improvement in data quality ● Constructed necessary dataset for alternative analysis with predictive and prescriptive based modeling techniques following exploratory data analysis necessary to determine the flow of data processing pipeline ● Spearheaded collaborative analyses across different research groups to integrate diverse expertise into a novel analysis, resulting in the expansion of the collaboration’s experimental scope ● Monitored real-time data acquisition to evaluate detector performance and identify anomalies to ensure data quality ● Standardized project documentation in GitHub for local laboratory, enhancing software accessibility and laboratory productivity ● Presented results at international conferences on behalf of entire collaboration following contributions to milestone publications
• Assessed discovery potential of model dependent signal given the constraints of observed data • Investigated novel algorithm for quantifying the background estimate in region of interest
● Assembled over 100 physics demonstrations for use in classroom and coordinated with faculty for organization of demonstrations to facilitate engaging lessons during physics lectures ● Motivated the public to learn fundamental physics concepts through performance of live demonstrations during outreach events