Data-driven marketing strategist with a passion for turning analytics into growth. Pursuing MS in Marketing Intelligence at Fordham's Gabelli School of Business.
Data-driven marketing curriculum focusing on customer insights, analytics, and strategic decision-making. Tools: Python, SQL, Tableau, Excel.
Active member of the Fordham Management Consulting Association.
Specialized in Psychology with coursework in counseling, organizational behaviour, and research methods.
Leadership roles in Outreach, Election Committee, and Psychology Society initiatives.
Awarded Dean's Merit Scholarship at Fordham University's Gabelli School of Business — recognizing exceptional academic achievement and leadership potential in the MS Marketing Intelligence program.
A rare combination of behavioral psychology and data-driven marketing creates a uniquely human-centered approach to consumer insights, brand storytelling, and campaign strategy.
Leading end-to-end marketing strategy for Halt Clinic's upcoming obesity medication practice in New York City. Conducting in-depth market analysis to identify trends, gaps, and competitive positioning in the weight management space — with a targeted goal of acquiring 1,000 patients within the first 12 months of launch.
Work spans patient focus groups, pharmacy partnership development, pamphlet design, social media management, and video production — creating a cohesive digital and community presence ahead of launch.
Led market research using 200+ survey responses and secondary data to evaluate pricing, product mix, expansion strategy, and customer engagement. Identified a 15% experiential marketing growth opportunity and delivered recommendations directly to the CMO — recognized for overall growth impact.
Analyzed cross-channel marketing performance in Tableau and Excel, tracking KPIs including Reach, Engagement, Conversion Rate, Funnel Performance, and ROI. Delivered actionable recommendations for campaign optimization and budget allocation.
Worked with sales and channel transaction data to answer key managerial questions about which marketing channels were driving the most revenue. Diagnosed and resolved multicollinearity issues across channel variables, then applied regression modeling to quantify each channel's contribution to sales — delivering data-backed budget allocation recommendations to optimize marketing spend and maximize ROI.
Performed end-to-end data analysis using Python (Pandas, NumPy, Matplotlib, Scikit-learn), evaluating KPIs across age groups and zones. Applied ANOVA and T-Test statistical modeling to deliver recommendations for pricing optimization, retention, and customer experience.