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I’m working on building a career as a Software Development Engineer in Test (SDET) by automating manual testing processes and creating robust test frameworks.
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Assessing your cultural and operational fit
PageObjectModel
February 9, 2025 – February 9, 2025
Basics of PageObjectModel with and without PageFactory
View ProjectDataDrivenTesting
January 26, 2025 – January 26, 2025
Automation script for passing values through excel and properties file to automate a functionality with test data
View Projectsaucelabsdemo-automation
January 12, 2025 – January 12, 2025
autoamtion for saucelabs website using pom as desgin pattern and BDD framework
View Projectorangehrm-automation-pom
December 30, 2024 – December 31, 2024
Automation framework for OrangeHRM using POM. Features include: Automated test scripts. Screenshot capture for failed tests. Logging mechanism for debugging. Detailed reporting for test results. Perfect for QA engineers automating OrangeHRM tests efficiently.
View ProjectAutomatic-Text-Suggestion-System-
June 11, 2021 – June 11, 2021
Imagine that you were a representative replying to customer online and you are asking more or less the same questions over and over to your customer. Would you like to get automatic suggestions instead of typing the same thing again and again? An autocomplete can be helpful, faster, and convenient and also correct any grammatical / spelling error at the same time. In the jupyter notebook in this project, we select a history of sentences written by the representatives and the customer, format and correct them using a few regex rules and count them so we can estimate their frequency and likeliness to be useful again. After the calculation of a similarity matrix based on the sklearn tfidf tool (frequency and normalization of words), we use this matrix to calculate the similarity between the new few words written by the representative and the history of messages written in the past. The Autocomplete will recognize the closest sentences and rank 3 final proposals:
View ProjectAutomatic-Text-Completion-Using-Nlp
February 27, 2021 – April 14, 2021
A system which revolves around the domains of NLP , a customer to representative interaction system that suggests a representative domain specific questions to interact quickly and efficiently with the user. It uses the tfidf model and a cosine similarity matrix to suggest the next few words based on the previous inputs
View ProjectCultural Fit Analysis
The candidate's projects show a mix of interests, with a focus on NLP/Data Science and some automation testing in Java. The NLP projects align well with a Data Scientist role, indicating a potential cultural fit for data-driven environments. The diversity of projects suggests a willingness to explore different technical areas. However, the lack of team projects or contributions makes it hard to assess collaborative fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to identify practical problems (e.g., automating customer service responses) and propose technical solutions. However, without psychometric or English test scores, it's difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration skills.