DHYEY DESAI

Working as a Data Science Co op at Starcycle, building scripts, data pipelines and dashboards to automate workflows, analyze datasets and support data informed product and operations decisions.

Head Teaching Assistant - DSCI 551: Foundations of Data Management (Prof. Wensheng Wu)

• Built RAG system with OpenAI Function Calling + Firebase, delivering actionable pet health analytics for cross-team insights. • Developed a multimodal AI pipeline that performed real-time speech transcription, PDF analysis, and summarization using TensorFlow and Transformers, which increased document accessibility and processing efficiency. • Created a visualization engine in Scala with over 12 chart types and caching mechanisms, which improved data representation speed and performance.

Head Teaching Assistant - DSCI 551: Foundations of Data Management (Prof. Wensheng Wu) - Lead a team of teaching assistants while supporting 100+ students in advanced data management concepts including cloud storage systems, data modeling, and distributed computing - Coordinate with faculty to develop and grade assignments focusing on relational databases, map-reduce paradigm, and data center architectures - Organize and lead weekly discussion sections to reinforce student understanding of complex data management techniques

Teaching Assistant - DSCI 351: Foundations of Data Management (Prof. Wensheng Wu) - Support undergraduate students in core database concepts including data modeling, relational databases, and NoSQL systems - Assist in grading assignments and providing detailed feedback on projects involving indexing, distributed file systems, and big-data analytics - Helping students master fundamental data management principles and practical applications

• Built AI-powered PO Automation conversational AI to analyze large purchase orders and enable multi-document querying. • Designed and optimized prompt sets to test system accuracy, relevance, and robustness in production environments. • Contributed to scalable deployment workflows, improving inference performance and reliability for enterprise use.

Worked on 4 research projects with university professors in Data Science and Machine Learning, focusing on Natural Language Processing (NLP), Computer Vision, and Image Processing. Improved data retrieval efficiency by 20% through the mastery of IT fundamentals and relational database management. Enhanced code efficiency and reduced development time by 20% using advanced Python techniques. Applied expertise in Python, Machine Learning, Pandas, Data Analysis, Object-Oriented Programming (OOP), and Software Engineering to solve complex problems and drive project success.

Having successfully completed my internship, I have gained substantial experience in various areas relevant to placement coordination. This experience has enriched my skill set, making me more adept at handling the responsibilities of a Placement Coordinator.

As the Head of Events for the Electronics Society at Manipal University Jaipur (MUJ), I spearheaded the planning and execution of various technical and non-technical events, utilizing my skills in qualitative research and data mining to enhance event outcomes. I led a team of volunteers, ensuring seamless collaboration and effective role assignment while maintaining close coordination with university administration to align our events with institutional policies. My role involved extensive budget management, where I optimized resources to deliver high-quality events, and stakeholder collaboration, securing sponsorships and coordinating with external vendors. This experience honed my leadership abilities and deepened my understanding of event management within an academic setting.

During my apprenticeship at PwC, I significantly improved data retrieval efficiency by 20% through a deep understanding of IT fundamentals and relational database management. By applying advanced Python techniques, I enhanced code efficiency and reduced development time by 20%. Additionally, I tackled complex problems using a combination of Python, machine learning, Pandas, data analysis, object-oriented programming (OOP), and software engineering principles, demonstrating my proficiency in these critical areas.

During my internship at the National University of Singapore, I collaborated with professors and a team of five on cutting-edge deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). My work significantly enhanced facial image analysis accuracy by 10% through the application of advanced model techniques. Additionally, I contributed to the Autism Detection project, boosting its performance by 10% using sophisticated facial image analysis methods. This experience allowed me to deepen my expertise in deep learning and applied research while achieving tangible improvements in project outcomes.

CUMULATIVE GPA : 3.73/4.0 First Semester (Fall 2024): - DSCI 551: Foundations of Data Management (GPA: 3.0/4.0) - DSCI 552: Machine Learning for Data Science (GPA: 4.0/4.0) Semester GPA: 3.5/4.0 Second Semester (Spring 2025): - DSCI 553: Foundations and Applications of Data Mining (GPA: 4.0/4.0) - DSCI 517: Research Methods and Analysis for User Studies (GPA: 3.7/4.0) Semester GPA: 3.85/4.0 Third Semester (Fall 2025): - CSCI 566 - Deep Learning and Its Applications (GPA: 3.7/4.0) - DSCI 560 - Data Science Professional Practicum (GPA: 4.0/4.0) Semester GPA: 3.85/4.0