I am a Data Science M.Sc. student at TU Dortmund University, where my academic work focuses on Computational Statistics, Machine Learning, and the theoretical foundations of probabilistic data analysis. TU Dortmund’s strong emphasis on statistical theory and computational modeling has provided me with a rigorous understanding of uncertainty quantification, inference, and model evaluation—core principles that guide modern data science.
Complementing this academic foundation, I bring several years of professional experience as a Software Implementation Engineer at Globit Global Information Technologies (Istanbul), specializing in Treasury Technology for financial institutions. My role has involved developing and maintaining high-performance trading infrastructures for banks and large-scale organizations across the EMEA region, where I designed automation pipelines, custom reporting systems, and integration frameworks using Shell scripting, T-SQL, Java (FIX protocol), and TIBCO middleware. I also contributed to the implementation of CI/CD pipelines and process automation to enhance the scalability and reliability of financial systems.
This dual background—rooted in both statistical methodology and software systems engineering—allows me to bridge the gap between analytical theory and computational practice. I am particularly interested in developing data-driven infrastructures that integrate robust statistical reasoning with modern software architectures, enabling scalable, interpretable, and reproducible solutions.
Through this interdisciplinary perspective, I aim to contribute to the next generation of computationally efficient and statistically principled data science tools, uniting insights from academic research and enterprise-level software engineering. This fusion of skills positions me at the intersection of theory and implementation—capable of navigating both the mathematical depth of data modeling and the technical demands of real-world systems.