Dilruba Topcuoglu

• Streamlined data collection methods to minimize analysis errors.
• Automated repetitive tasks using scripting languages such as Python and SQL, saving time during analytical process significantly.
• Utilized advanced querying, visualization and analytics tools to analyze and process complex data sets.
• Conducted feature engineering efforts to enhance model performance by creating new relevant variables from raw input data sources.
• Conducted a comprehensive segmentation analysis on customer data by applying techniques such as k-means clustering to identify distinct data segments.
• Designed and implemented a robust sales forecasting system using advanced time series algorithms (ARIMA, ARIMAX, SARIMA, SARIMAX,boosting algorithms and statsforecast)
• Simplified complex data sets into actionable insights for executive decision-making, enhancing strategic planning.
• Developed product association recommendation models using techniques such as Market Basket Analysis and Apriori algorithm, providing data-driven insights for cross-sell and upsell strategies.
• Built employee performance and churn prediction models [specific techniques: logistic regression, decision trees, XGBoost], enabling proactive retention strategies and improving workforce management with an accuracy of over %75.
• Increased team collaboration with implementation of agile methodologies, facilitating cross-functional team engagement.
• Conducted meetings with various business units, comprehensively assessing their needs and leveraging data-driven insights to formulate impactful project solutions.
• Translated business requirements into data-driven solutions, providing value-added insights that directly contributed to the organization''s strategic goals.
• Identified, measured and recommended improvement strategies for KPIs across business areas.

• Python Programming for Data Science
• CRM Analytics
• Measurement Problems
• Recommendation Systems
• Feature Engineering
• Machine Learning
• Optimization

Completed a 3-month theoretical and practical training program. Gained hands-on experience by developing models to solve real-world problems using real datasets.