Sonal Agarwal

Background

About Sonal Agarwal

Currently pursuing an MSc in Data Science at the University of Bristol, developing strong expertise in machine learning, large-scale data engineering, and advanced analytics. • Data Science and IT professional with a B.Tech in Information Technology, complemented by hands-on experience in software development, cloud infrastructure, and technology operations. • Professional experience includes: TechOps Engineer at Thales: Led system performance optimization, cloud automation, and service reliability improvements for global users. Software Developer at KPMG: Designed intuitive, responsive web interfaces to enhance financial data accessibility and user experience. Full Stack Developer at TCS: Contributed to developing a CFO Board website and FAQ section used by users, streamlining critical information access. • Technical skillset includes Python, AWS, Docker, Kubernetes, Tableau, machine learning, natural language processing (NLP), data visualization, JavaScript, HTML/CSS, MySQL, Git, and Linux environments. • Strong communicator and collaborator, adept at problem-solving, adapting quickly to new challenges, and working effectively within cross-functional teams. • Key projects- Exploring Socio-Economic Trends in England and Wales: Utilized Tableau, Bayesian methods, PCA, and t-SNE to analyze UK Census data, generating actionable insights on employment and diversity patterns to inform policy decisions- Text Analytics for Climate Disclosures and Social Media: Built NLP models including transformer-based ClimateBERT for sentiment analysis and entity extraction, demonstrating expertise in handling unstructured data and advanced machine learning techniques- COVID-19 Web Scraper & Voice Assistant: Developed Python-based tools for real-time data scraping and voice interaction, showcasing skills in automation, API handling, and user interaction design- Cloud-Native Data Analytics Application: Created a scalable AWS and Docker-based analytics platform automating data pipelines and integrating multiple data sources for real-time processing and visualization- DevOps Pipeline Automation: Implemented automated CI/CD pipelines with Jenkins, GitHub Actions, and Docker, improving deployment efficiency and reducing errors in production environments.