Data Scientist Who Speaks Human
Turning complex data into decisions people actually use.
Technical + Communicative + Business-Aware
Hey there! I'm Alexandra, a Data Science student at NYU (graduating May 2026) who loves finding the story hidden in the numbers. Unlike the stereotypical "data scientist stuck in a cubicle," I thrive in roles where I can collaborate across teams, communicate insights to non-technical stakeholders, and drive business impact.
What makes me different? I'm a technical translator. I can build ML models and wrangle messy datasets, but I'm equally comfortable presenting findings to executives or working directly with clients to solve their problems. My background spans enterprise data governance at Sallie Mae, AI model development, and real estate analytics—all united by a passion for making data accessible and actionable.
I'm fluent in English and Spanish, conversational in Mandarin, and always eager to learn. When I'm not diving into datasets, you'll find me working as a Special Events Assistant for NYU Athletics (yes, I analyze ticket sales and fan engagement data there too!).
What I'm looking for: Client-facing analytics roles, product data science, technical consulting, or any position where I can blend technical depth with business strategy and human connection.
Real-world data science work that drives business decisions
End-to-end analytics pipeline analyzing 530 films (2019-2024) using TMDb API, SQL, and Tableau. Discovered Action films achieve 2,437% ROI theatrical-only, while Crime films gain 582% from streaming releases.
Impact: Insights could inform $10-50M release windowing strategies for major studios. View Live Dashboard →
Analyzed regional music preferences across major US cities by integrating Spotify Web API, Last.fm API, and US Census data to identify geographic patterns in musical taste.
Impact: Enables hyper-local marketing strategies for streaming platforms and concert promoters.
Supported Chief Data Office in implementing enterprise-wide data governance initiatives. Built Python API integrations and enhanced metadata accuracy in Alation.
Impact: Strengthened data accessibility and decision-making across the organization.
SQL-based analysis of Google Analytics 4 e-commerce data (270K users, $362K revenue) uncovering critical retention crisis: 93.6% customer churn after first purchase. Analyzed cohort behavior, marketing attribution, and revenue drivers to deliver actionable recommendations.
Impact: Identified $17.7K+ revenue recovery opportunity through Month 1 re-engagement campaigns and optimized marketing channel allocation.
Analyzed 90K mobile game players to test gate placement impact on retention (Day 1: 44.8% vs 44.2%, Day 7: 19.0% vs 18.2%). Used bootstrapping and statistical testing in R to determine optimal player experience strategy.
Impact: Provided data-driven recommendation on feature placement affecting user retention. View Full Analysis →
Built image classification model using transfer learning (ResNet18 & VGG13) to classify 102 flower species with 60%+ accuracy. AWS Scholarship project.
Skills: PyTorch, Transfer Learning, GPU Optimization.
I'm not just a tool collector—I use these skills to solve real business problems
I'm actively seeking client-facing analytics, product data science, technical consulting, or cross-functional data roles starting Summer/Fall 2026.