Alessia Giardinelli Pesce

Strategically enhancing Coty’s consumer beauty products and brands' positioning across key markets. Conducting machine learning research to improve e-commerce PDPs conversion rate. Focus on Innovation (NPDs) and Communication recommendations.

I conducted sell-out and competitor analyses for Gucci Beauty and Marc Jacobs Fragrances products, leveraging data to optimize performance and drive strategic decision-making. Additionally, my role involved promotional execution, collaborating with key retail partners such as Douglas and ICI PARIS XL.

As the Global Head of Marketing at the United Italian Societies (UIS), I led the organization's marketing efforts across six countries, bridging local and global initiatives. My role involved: 1. Content Strategy & Coordination: Overseeing the production of both local and global content, ensuring alignment with UIS's mission and objectives. Local content includes events and initiatives tailored to specific countries, while global content encompasses UIS services, communications, and participation in international events. 2. Team Leadership: Managing and supporting local Heads of Marketing across different countries, empowering them to produce and promote country-specific content effectively. 3. Collaboration & Resource Allocation: Equitably distributing global content responsibilities among local teams, fostering collaboration and consistency across regions.

Collaborated with UNICEF Nederland on a machine learning research project aimed at increasing social media engagement. Led data-driven strategies to optimize content performance, leveraging predictive modeling and analytics. Among six competing teams, my team’s work was recognized as the best for delivering the most impactful insights.

Supply chain planning Logistics Support SnD modelling Data analysis on shipment line ups Admin/invoicing Software skills (Agiblocks)

My thesis investigated the extraction of emotional arcs from Instagram influencers’ captions using Large Language Models and analyzed how effectively these arcs predict engagement through decision tree and random forest models.

Thesis Grade: 8.00