Curriculum Vitae
EDUCATION
PhD in Probabilistic Machine Learning in Healthcare Management
Cardiff University, Cardiff, UK Oct 2024 — Present
PhD in Probabilistic Machine Learning in Healthcare Management
- Recipient of WGSSS‑ESRC Studentship Award
- Project focused on enhancing discharge care coordination in healthcare and social care using a probabilistic data-driven modelling approach
- Supervisors: Prof Bahman Rostami‑Tabar, Dr. Jeremy Dixon
MSc in Financial Mathematics
Middle East Technical University, Ankara, Turkey Oct 2017 — Aug 2021
- Dissertation: Effects of Exchange Rate Volatility and Firm-Specific Features on the Rates of Returns of the Manufacturing Firms Listed in Borsa İstanbul: A CAPM Approach
- Statistical&Machine learning techniques used: Markov Switching GARCH Models, ARIMA, Panel Data Econometrics, Principal Component Analysis
BSc in Business Administration
Middle East Technical University, Turkey Oct 2011 — Aug 2015
WORK EXPERINCE
Reporting and Data Analytics Executive
AKBANK (a leading bank in Turkey), Istanbul Jun 2022 — Sep 2024
- Applied time-series forecasting and machine learning techniques, including ARIMA, Bayesian Time Series, Prophet, ANN, LSTM, Random Forest, LightGBM and XGBoost, to historical data for making long-horizon forecasts of daily customer call volume. The best model achieved over 94% accuracy (1-MAPE) in forecasting all days of the next month.
- Implemented machine learning models (e.g., XGBoost, LightGBM, CatBoost) to predict customer behavior with an over recall rate of 70% and an accuracy rate of 85%, enabling fewer customer complaints and increase sales by 14%.
- Analyzing large amounts of data to identify trends and find patterns, signals and hidden stories within customer calls data.
- Applied machine learning techniques (Z-score, IsolationForest) to detect anomalies.
- Applied unsupervised machine learning techniques (DBSCAN, Gaussian mixture, K-means) to cluster customers.
- Hyperparameter tuning for machine learning models using Hyperopt, Optuna and KerasTuner.
Research Associate
The Economic Policy Research Foundation of Turkey (Think Tank), Ankara Jan 2022 — Apr 2024
- Determined areas of research to increase knowledge in the particular field.
- Utilizing inferential statistics such as hypothesis testing (e.g., t-test, ANOVA test, population proportion test), confidence intervals, correlation analysis and regression analysis to make inferences and draw conclusions about data.
- Developed statistical models (regression analysis, panel data modeling) for regional development projects to contribute to data-driven decisions.
Senior Process Development Analyst
ETI GIDA Inc (a major FMCG player in Turkey), Eskisehir Jun 2019 — Jan 2022
- Interacted with internal customers to understand business needs and translate into requirements and project scope.
- Assessed the impact of current business processes on users and stakeholders and evaluated potential areas for improvement.
- Maintained strong working knowledge of ERP (SAP), CRM and business intelligence tools and operational features.
Internal Auditor Ankara, Turkey
Turk Telekom Inc. (the telecom giant of Turkey), Ankara Nov 2015 — Jun 2019
- Performed strategic planning, execution and finalization of audits using data analytics and critical thinking skills.
- Investigated discrepancies discovered during the auditing process.
- Recommended new methods to improve internal controls and operating efficiency.
RESEARCH INTERESTS
- Data-driven decision making
- Machine learning
- Reinforcement learning and stocastic optimization in decision making
- Time series forecasting
- Probabilistic forecasting
- Conformal prediction for time series forecasting
SKILLS & EXPERTISE
Expertise: Mathematical and Statistical Modeling, Machine Learning, Time Series Analysis and Forecasting, Stocastic Optimization and Reinforecement Learning, Statistical and Explanatory Data Analysis
Programming: Python, SQL
Reporting: Quarto, Advanced Excel, QlikView, SAS
Languages English (Fluent), Kurdish (Native), Turkish (Native)
PROFESSIONAL DEVELOPMENT
- Certification:
- Books (some of my go-to books):
- Forecasting: Principles and Practice (Hyndman et al, 2021)
- Introduction to Statistical Learning with Applications in R (Tibshirani et al, 2019)
- The Elements of Statistical Learning (Tibshirani et al, 2008)
- Probabilistic Machine Learning: An Introduction (Murphy. 2022)
- Reinforcement Learning: An Introduction (Barto et al, 2018)
- Reinforcement Learning and Stochastic Optimization (Powell, 2022)
- Time Series Forecasting in Python (Peixeiro, 2022)
- A Student’s Guide to Bayesian Statistics (Lambert, 2018)
- Dive into deep learning (Zhang, 2022)
References
Available upon request