Jeel Shah

Experience

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  KJ Somaiya College of Engineering, Vidyavihar

  Feb 2020 - Feb 2020

  Web Developer
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  KJ Somaiya College of Engineering, Vidyavihar

  Dec 2021 - Feb 2022

  Software Engineer

  Enhanced user experience by developing a dynamic scheduling system utilizing Django, leading to a more intuitive and responsive user interfaceOptimized the scheduling system by integrating Machine Learning for task distribution, ensuring effective decision-making aligned with staff and faculty preferences

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  The Sparks Foundation

  Jan 2022 - Jun 2022

  Data Science and Business Analytics Intern
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  Analytica

  Mar 2022 - Jun 2022

  Data Analytics Intern

  Developed a hybrid recommendation system using a combination of machine learning algorithms (collaborative filtering and content-based filtering), achieving a 40% increase in user engagementManaged and analyzed large-scale customer datasets for pattern recognition and predictive modeling, leveraging big data(MongoDB) to extract actionable insightsConducted comprehensive customer behavior analysis using Python and Tableau, enhancing strategies for targeted marketing

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  Scholarship Auditions

  Sept 2023 - May 2024

  Data Science

  Built and optimized ETL data pipelines to automate data acquisition, reducing manual data cleaning efforts by 60% and enabling faster data preparationUtilized AWS for data storage and automated ETL processes, increasing data pipeline efficiency by 30%Designed and developed RESTful APIs using React, ensuring high scalability and performance for email marketing softwareCollaborated with cross-functional teams, to design and develop user-friendly screens using Figma, improving customer satisfactionProactively resolved runtime errors in front-end components, reducing production issues by 50% and enhancing application reliability Show less

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  The Research Foundation for SUNY

  Jan 2024 - now

  Machine Learning Research Assistant

  Machine Learning Research Assistant at The Research Foundation for SUNY for SunThru LLC Developing Machine Learning model to predict failure of the production of aerogel monolith machines, extracted using sensor data (weather, engine, temperature, pressure) from Dataq Instruments (WinDaq).Collaborating with the engineering team to integrate real-time monitoring and maintenance strategies into machine learning models