

- Location
- Brampton, Ontario, Canada
- Bio
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QA Analyst with 3+ years of experience in software testing, defect tracking, and automation. Currently volunteering as a Junior Web Developer at Gross Innovation Trizan, Canada, expanding my skills in front-end development. Completed a QA internship at RacadTech, Canada, Fintech training with Lighthouse Labs, and pursuing Data Analytics with Python at McMaster University. Holds a Bachelor's degree in Computer Applications.
I bring strong problem-solving, communication, and collaboration skills, with a proven ability to adapt to new technologies and work effectively in team environments. My attention to detail and commitment to continuous learning ensure I consistently meet project goals and contribute to successful outcomes. - Resume
- JOSINA JOSE.pdf
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Toronto, Ontario, Canada
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Recent projects

AI-Powered LinkedIn Lead Generation Tool
Trillagy aims to enhance its business development efforts by leveraging LinkedIn's vast professional network. The goal of this project is to develop an AI-powered tool that automates the process of identifying and generating high-quality business leads on LinkedIn. The tool should analyze LinkedIn profiles and activity to identify potential leads that match Trillagy's target audience criteria. By integrating AI, the tool will be able to learn and improve its lead generation accuracy over time. This project will allow learners to apply their knowledge of AI algorithms, data analysis, and software development to create a practical solution for real-world business challenges. The project will involve tasks such as defining lead criteria, developing algorithms for profile analysis, and creating a user-friendly interface for Trillagy's business development team.

Coordinator - Client Relations
1. Testing and update on the sales funnel flow for potential clients: clear documentation and resources in place 2. Efficient and easy to use setup of Zoho learning and support platform as a subscription product for clients. Confirming efficiency and scalability