Data Science with less than a year in Applied Computing with experience in Data Analytics, Machine L
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Master's student in Applied Computing with experience in data analytics, machine learning, and large-scale data processing. Proficient in Python and SQL for analyzing structured and unstructured datasets to generate actionable insights. Experienced in developing dashboards, performing exploratory data analysis, and communicating analytical findings to stakeholders. Passionate about leveraging analytics, AI, and data storytelling to support risk analysis, decision-making, and enterprise insights.
University of Windsor
Master's in applied computing · Applied Computing
August 1, 2024 – June 30, 2025
MBIT - Gujarat Technological University
Bachelor of Computer Engineering · Computer Engineering
August 1, 2019 – June 30, 2023
Infolabz IT Services PVT
Data Science Intern
January 1, 2023 – June 1, 2023
Ahmedabad, Gujarat, India
AI Agent Scalability Optimization on Linux
September 1, 2025 – December 1, 2025
Designed and optimized a multi-agent AI system using AutoGen and vLLM to analyze scalability and performance of large-scale AI workloads. Conducted performance profiling and system analysis using Linux tools (perf, ftrace, htop) to identify system bottlenecks and improve resource utilization. Improved system scalability from 25 to 500 concurrent agents, achieving 22-30 tasks/sec throughput with stable GPU utilization.
Research: Deepfake Sentinel
January 1, 2025 – April 1, 2025
Developed an AI-based deepfake detection system with 92% accuracy by fine-tuning a hybrid GenConViT model combining CNN and Transformer techniques. Built Flask APIs to enable real-time deepfake classification for media verification tasks. Integrated MongoDB to store detection results and support dynamic model retraining; showcased the project as a scalable solution at Demo Day.
Analytics: EcoMetrics
September 1, 2024 – December 1, 2024
Built an interactive Power BI dashboard analyzing waste generation and recycling trends across Canadian municipalities. Processed and analyzed 10,000+ geospatial records using Python (Pandas, NumPy) and Google Maps API. Identified high-emission regions and operational gaps, delivering data-driven insights for sustainability decision-making. Transformed complex datasets into intuitive visualizations to support stakeholder understanding and policy insights.
Sentiment Analysis Based Recommendation System
May 1, 2023 – July 1, 2023
Developed a recommendation system leveraging sentiment analysis, delivering personalized product suggestions based on user reviews, resulting in a 47% increase in customer engagement. Utilized web scraping and APIs for data collection, employing NLP techniques to classify sentiments and extract key features.
Microsoft Azure
Unknown
March 1, 2025 – Present
Selenium Essential Training
Unknown
October 1, 2024 – Present
Data Analyst with Python- Datacamp
Datacamp
December 1, 2022 – Present
Data Science with Python- Datacamp
Datacamp
February 1, 2022 – Present
Cultural Fit Analysis
The candidate's academic background and project diversity indicate a strong interest in cutting-edge AI and data science applications. Their involvement in projects like 'Deepfake Sentinel' and 'AI Agent Scalability Optimization' suggests an innovative and research-oriented mindset. The 'EcoMetrics' project also shows an interest in applying data science for social and environmental impact, which aligns with a culture that values purpose-driven work. The certifications further demonstrate a commitment to continuous learning and professional development.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying new technologies, as evidenced by diverse academic projects and certifications. Their experience in collaborating with cross-functional teams during their internship suggests an ability to work effectively in a team setting and translate technical findings into business insights. The project descriptions highlight problem-solving skills and a results-oriented mindset, such as improving system scalability and achieving high detection accuracy.