London School of Economics

project where I applied K-means clustering to segment customers and analyse market trends, showing me how data-driven insights support business decisions. During COVID-19, I led the development of a machine-learning healthcare portal to address pricing opacity in private hospitals, later published in a peer-reviewed journal, reinforcing that analytics can create social impact when aligned with real needs. Alongside academics, as secretary of my department's student association I led technical events for 200+ participants, and at a national hackathon I led a team that turned real-time vaccine and hospital data into a decision-ready COVID-19 dashboard. Professionally, early roles at a data-analytics firm and a robotics company let me work with industrial data, dashboards, and automation tools; currently, as a global network operations engineer at Company A (a large communications company), I manage services for 50+ enterprise clients and built a Python- and API-based automation framework for firmware upgrades with structured data logging for performance analysis. Now I aim to transition into a data-scientist role solving complex business problems with advanced analytics, particularly in retail and e-commerce, and in the long term to build a data-driven consulting firm that helps businesses optimise demand forecasting, supply chains, and customer experience. LSE's MSc Data Science offers the exposure and depth I need: electives such as Advanced Time Series Analysis, Unsupervised Machine Learning, and Distributed Computing for Big Data will help me build scalable predictive models, while Algorithms and Computation will sharpen my optimisation skills. Beyond the classroom, I look forward to the Data Science Society and the Volunteering Centre, applying my skills to social initiatives. Conversations with alumni confirmed LSE's collaborative culture, academic rigour, and global outlook as the right next step.