Data professional and recent MBA graduate with 4+ years of end-to-end analytics experience across customer intelligence, product, and strategy. I translate complex, ambiguous datasets into decisions that move business forward.
I'm an MBA graduate from Babson College (Business Analytics & Machine Learning, STEM designation) with a foundation in Econometrics and International Business from UC San Diego. I've spent the last 4+ years solving analytical problems from scoping through delivery — across operations, customer intelligence, and product strategy.
My toolkit covers the full analytics stack: from writing optimized SQL pipelines and Python churn models to building executive Power BI dashboards and deploying machine learning classification frameworks. I've worked across Fortune 500 supply chains (DHL), Big Four professional services (PwC), SaaS startups (GoSite), and enterprise tech (Staples).
I'm fluent in English, French (DALF C1), and Portuguese, which has allowed me to contribute to multilingual analytical projects across global client bases. I thrive in ambiguous problem spaces where first-principles thinking and data-driven rigor are required to define success metrics and build the infrastructure to measure them.
I'm actively seeking roles in Data Analytics, Data Science, and Strategy & Operations within the tech sector — particularly in health tech, consumer tech, and SaaS.
Built a Whoop-like recovery tracking app during a 24-hour Babson Buildathon. Parsed 2GB of Apple HealthKit XML data using Python/pandas. Engineered custom recovery scoring algorithms analyzing 30-day HRV, resting heart rate, and sleep baselines. Integrated an AI chatbot that learns from user telemetry to deliver personalized strain and rest recommendations.
Built a multi-model ML framework to classify 7 forest cover types from U.S. Forest Service cartographic data across 15,120 observations. Consolidated 44 binary indicators, engineered distance/elevation interaction features, and benchmarked KNN, Logistic Regression, and Random Forest. Final Stacking Ensemble (Neural Network meta-model) achieved 85.6% accuracy — an 83% error reduction over baseline.
Engineered an enterprise BI system mapping stakeholder user stories to interactive analytical views. Built T-SQL extraction scripts in SSMS to isolate clean DIM/FACT tables. Authored DAX measures linking disconnected Excel budget models. Deployed a 3-page Power BI platform: Sales Overview KPIs, Customer regional maps, and Product lifecycle trend analytics.
Designed a complete BI portfolio ecosystem for a fictional international manufacturer. Transformed raw transactional data into star schemas (Territory, Customer, Product matrices). Authored sophisticated DAX blocks for rolling return rates, target variances, and adjusted profit margins. Built tooltips, geographic maps, localized filters, and automated snapshot updates across a 5-view interactive dashboard.
Partnered with executive leadership at a global medical device manufacturer (Babson Consulting Experience). Analyzed proprietary customer segmentation data across 4 personas to map behavioral patterns and willingness-to-pay. Interviewed ostomy patients to identify procurement friction points. Quantified an 18.8% CAGR market opportunity and delivered a 3-tier subscription architecture (Basic, Premium, Full Care) with a 9-month phased MVP roadmap and LTV testing framework.
