- Global Antifraud Solution: Implementation and performance monitoring of the project for detecting fraud in MOD/MTPL claims carried out by Allianz Global. Used Technology: Python, Sklearn, XGBoost, MLFlow- Pension Churn Prediction: Identify high-risk customers related to the Churn model and take appropriate measures in this regard in correlation with customer value. The project include extracting the impacts of the actions taken with the results of the model and A/B testing. Used Technology: Python, Sklearn, XGBoost, CatBost, Optuna, LGBM, Shap, Livy, Spark, Hive, Sqoop, Oozie- CLTV Project : Calculating the CLTV value of customers using churn and crossel probabilities found using machine learning algorithm. Used Technology: Python, Sklearn, LGBM, Livy, Spark, Hive, Oozie- Template Project: It is a project developed for use in Data Science projects. It includes software architecture such as class structure, code structure, project structure. Used Technology: Python, Pandas ,Sklearn, LGBM, Livy, Spark, Hive, Oozie- Digitangelo: It is an A Customer AI Assistant that communicates interactively with customers with its Chain of Thought structure. Used Technology: Python, LangChain, OpenAI, Streamlit, Flask- Developing ETL Process: Transferring the data in the core systems to the Hadoop environment by scheduling it at certain periods. Used Technology: Hive, Hue, Sqoop, Oozie- Administrative Task on Big Data Platform: User Management, Training Support and Platform Management

Analyzing and realizing RD projects related to data science.- Voice Signal Detection : At the time of the call, the voices of the customer representatives are analyzed and statistically determining the low voice, loud voice and silence situations. Used Technology: javascript- Workforce Managment : Using machine learning models, hourly call estimation, aht estimation and required number of agents were estimated for the relevant time period. Shift planning was carried out under certain constraints determined by the user, using the estimated number of agents. Used Technology: Python, LGBM, optapy, pyworkforce

Analyzing and realizing RD projects related to data science.- Voice Signal Detection : At the time of the call, the voices of the customer representatives are analyzed and statistically determining the low voice, loud voice and silence situations. Used Technology: javascript- Workforce Managment : Using machine learning models, hourly call estimation, aht estimation and required number of agents were estimated for the relevant time period. Shift planning was carried out under certain constraints determined by the user, using the estimated number of agents. Used Technology: Python, LGBM, optapy, pyworkforce

Main Responsibilities as a Software Engineer:Development of algorithms and software tools that help automated machine learning process. - Anomaly Detector : Anomaly Detector project has been developed to find anomalies on any time-series data. Used Technology: Java, Maven, Spring Boot, Python, Django, Dash, Jenkins - AI Platform: It is a project consisting of 78 projects and includes artifcial intelligence workflows. Used Technology:Visual Studio, C#- Intelligent Data Ready:It is a project that does data preprocessing by generating automatic SQL. Used Technology: Java, Maven, Spring Boot, Jenkins -AI Platform Dashboard: Dashboard showing the results of the models created as a result of the AI platform. Used Technology: R, Shiny - Autonon : It automates the Machine Learning process steps to be applied to real-life problems. It contains automoted feature extraction, data quality control and machine learning modules. Used Technology: Python, numpy, pandas, cython, LGBM, scikit learnMain Responsibilities as a Data Scientist:- Designing ETL Process - Data Quality Control- Fraud Detection- Automatic Feature Extraction- Building ML Models, monitoring model performance

- PyCharm, Virtual Envs, Dependency Management, Git- Measurement Problems (Dynamic Pricing, AB Testing, Sorting)- Demand Forecasting Time Series- CRM Analytics (KPI, Cohort, CLTV, Segmentation)- BG-NBD: Expected Sales Forecasting- Gamma-Gamma: Expected Average Profit- RFM Analysis- Churn Analysis- Recommendation Systems (ARL, Collaborative Fil., MF)- Advanced Feature Engineering- Script Level Machine Learning- House Price Prediction Model- Automation and Model Deployment (Flask, makefile)- NLP (TM, Review Modeling, Word Embeddings)- PySpark, MS SQL- Docker, Google Cloud, Network, Agile & Scrum- Home Credit Default Risk Predict

SST-Füze Sistem Mühendisliği

Solution Engineering