Mehmet Yalcin
Aspiring Data Engineer | Applied Economics & Data Science Student
Transforming raw data into well-modelled, reliable datasets that drive business decisions.
About Me
I am a highly motivated and detail-oriented professional with a strong foundation in economics, currently advancing my expertise through a Master's in Applied Economics and Data Science. My goal is to leverage my analytical and technical skills to excel as a Data Engineer. I am passionate about building and maintaining data pipelines, ensuring data quality, and collaborating with cross-functional teams to solve challenging business problems.
My journey has taken me from a Bachelor's in Economics to a BI consultancy role and now to advanced studies in Germany, all driven by a core interest in the power of data. I thrive in team-oriented environments and am eager to contribute to a company that values innovation, growth, and a data-driven culture.
Core Competencies
This chart provides an overview of my technical skills, categorized for clarity. I am always expanding my knowledge base and am a quick learner with new technologies.
Professional Journey
My career path reflects a progressive journey into the world of data, from foundational analysis roles to more complex development and business intelligence positions. Each role has provided me with valuable experience in data processing, analysis, and collaboration.
Research Assistant ML Developer
Carl von Ossietzky Universität Oldenburg | April 2025 - Present
- Developed data processing solutions for an mHealth project on fall prevention.
- Implemented and optimized MATLAB scripts to analyze biomechanical responses.
- Processed over 1,400 perturbation segments, enabling pattern identification.
- Improved code efficiency, reducing data processing time by 40%.
BI Consultant
NTT DATA Business Solutions, Istanbul | Oct 2022 - Oct 2023
- Provided end-to-end technical solutions based on deep business understanding.
- Built data models and visualizations using SQL, Qliksense, and PowerBI.
- Delivered over 100 BI dashboards, reducing manual reporting by 9 hours weekly.
- Collaborated closely with vendors and internal IT and Business teams.
Data Manager Assistant
Bayraklı Belediyesi, İzmir | Aug - Oct 2019
- Assisted with auto-classification of mapping documents using SAP and Tableau.
- Dramatically improved the SLA for the classification process from 50 minutes to 2 minutes per document.
Economical Data Analyst Intern
Capital Markets Board of Turkey, Ankara | Jul - Aug 2018
- Analyzed economic data and stock market trends.
- Wrote Python routines to collect datasets for analysis.
Featured Projects
These projects showcase my ability to apply technical skills to solve practical problems, from machine learning to database design and business intelligence. Use the filters to find projects with specific technologies.
✨ Click the "Generate Business Impact" button on each project card to see how AI (LLM) articulates the business value and strategic importance of my work!
Machine Learning for Sports Classification
Developed a CNN model to classify sports images, achieving 97.6% test accuracy. Focused on transfer learning, data augmentation, and addressing class imbalance.
Yildirim Holding BI Development
Developed Qlik Sense dashboards and system monitoring tools. Connected to SAP data sources using the Qlik Rest API Connector to provide critical business insights.
Hotel Booking Database
Designed and built a comprehensive hotel database from the ground up. Implemented complex features like transaction/trigger structures for payment systems and database indexing.
Academic Data Visualization Project
Analyzed the relationship between voting rates and community landmarks in Turkey. Collected data via web scraping using RPA (UiPath) for article research.
Football Transfer Success Prediction
ML models predicting player performance after transfers. Achieved R² 0.94 for goals prediction and 98% accuracy for improvement classification using Random Forest and XGBoost on 1,483 transfers.
Transfer Economic Efficiency Analysis
Analyzed value-for-money in football transfers. Found only 19.8% achieve good efficiency, with transfers >50M being 27% less efficient. Identified Serie A as most efficient league.
F1 Race Position Prediction
Predicts F1 driver finishing positions using qualifying results, team performance, and weather data. Achieved R² 0.63 and MAE 2.24 positions with enhanced feature engineering including race pace analysis.
Pass Success Prediction
Predicts football pass success using StatsBomb event data and Kloppy library. Achieved 80.4% accuracy with LightGBM (10% better than baseline), analyzing 6,723 passes with 48 engineered features including pressure, distance, and field position.
Turkish Super League Match Prediction
Predicts match outcomes (Home/Draw/Away) using 60 years of Turkish football data (17,408 matches). Achieved 51.3% accuracy with Random Forest, outperforming baseline and demonstrating strong home win prediction (84% recall).
Interactive Data Story: Machine Learning for Sports Classification
Explore the journey of my machine learning project, from defining the problem to understanding its impact. Click 'Next' to proceed through the story steps.