Yigit Eydemir

+Managed the end-to-end lifecycles from data logic design (Python) to early-stage frontend integration (React) +Led requirements engineering and architectural planning for a health tourism application, translating business ideas into technical specifications. +Developed company websites, covering requirements gathering, implementation, and deployment using PHP, HTML/CSS, SQL +Designed relational data models (PostgreSQL) and application workflows to ensure data integrity and system reliability.

+Developed web applications and data-driven dashboards using WordPress, Wix, and SQL to optimize recruitment and acquisition funnels. +Prepared and delivered internal team trainings on SQL to foster a data-driven culture. +Provided modern Agile and Scrum methodology trainings to accelerate cross-functional execution and managed project stages/ticket lifecycles via Kanban boards.

+Collaborated with business analysts to optimize workflow processes using Softexpert Workflow Tool turning operational bottlenecks into automated workflows. +Developed and provided technical support for workflow management and process optimization. +Designed and implemented flow forms to streamline business operations. (Softexpert) +Utilized SQL to manage and manipulate data for workflow and reporting needs. +Implemented and managed project tracking systems within the IT department. (Jira & Trello)

+Analyzed complex enterprise business processes to identify automation opportunities, aligning workflows with continuous integration and efficiency principles using UiPath (RPA). +Gained foundational understanding of enterprise ERP systems (SAP) and large-scale process automation concepts.

Master’s Thesis: Estimating Saturation in Grounded Theory Studies with Species Richness Estimation: Built an analytical ETL & data pipeline to estimate theoretical saturation. Focused on data validity and preprocessing strategies using Python and R scripts, Git, and Species Richness Estimators under a TDD mindset.

Bachelor Thesis: Pattern Recognition and Tracking of Active Bipolar Magnetic Regions on the Sun: Applied machine learning and deep learning methods to analyze solar images and magnetogram data from SDO and SOHO spacecraft