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As a Student Automation Developer, you will play a critical role in ensuring the quality, stability, and success of our core platform. This is not just a background role - you will take ownership of end-to-end test plans for complex, cutting-edge features (including AI sessions, data privacy tools, and query builders).

You will work closely with experienced developers, Product Managers, and Data Scientists, gaining deep technical exposure that serves as an excellent stepping stone toward a future career in FS or BE software engineering.

Key Responsibilities
Automation Development: Design, build, and maintain robust automated testing frameworks and scripts, primarily using Python.
Feature Ownership: Take the lead on creating and executing comprehensive test plans for newly released features, ensuring product stability from day one.
CI/CD Pipeline Management: Monitor daily CI/CD pipelines to catch regressions early, triage failures, and maintain a highly reliable release cycle.
API & Backend Testing: Conduct deep backend validation by interacting with REST APIs, utilizing tools like Postman and internal automation clients.
Cross-Functional Collaboration: Partner directly with the Platform, Product, and Data Science teams to clarify requirements, investigate bugs, and define expected system behaviors.
Continuous Improvement: Actively participate in framework refactoring, workflow improvements, and writing clean, maintainable code.

We are looking for a motivated and enthusiastic Data Science Student to support our AI and data management efforts. The role involves data collection and preparation, Deep Learning models architecture design, implementation, training, and evaluating algorithm results mainly in the medical imaging domain. Additionally, you will help with other data-related tasks as needed to support our operations. What you will be doing : Data Collection: Gather, organize and prepare (including augmentation) data from various storage locations using tools such as Excel and Python scripting. Develop python code for automatic data collection. Deep Learning Models – Implementation and Training: Analyze to decide what kind of model to use, implement it, train it and adapt to improve the model results. Evaluate Model Results: Visually and empirically assess AI model and other computer vision algorithms outputs, providing feedback and scoring to improve model performance. Assist in Other Data Related Tasks: Organize data from different sources, clean it and implement automatic data collection and preparation pipeline scripts. · Working according to Insightec Quality Policy, relevant Quality Procedures & Standards, and privacy & safety rules.