Sahin Cetin

• o Analyzed financial data flows across finance and retail systems to assess revenue recognition accuracy and identify data quality issues in different markets.
• o Supported cyber incident response audit by reviewing policy documentation, analyzing incident reports, and assessing escalation and recovery procedures.

• o Utilized Python and SQL to analyze datasets, identifying sales trends and stocking inefficiencies for an apparel store. These analyses provided actionable insights for optimizing product assortment and managing inventory.
• o Engineered and automated data pipelines and ETL processes in Python, leveraging PySpark for processing,
• o Developed a comprehensive visitor behavior analysis dashboard for a healthcare equipment website, using Power BI to track key metrics such as user flow, bounce rates, and session duration.

One of the leading publicly traded private banks in the country, with over 15,000 employees. It is widely recognized for its robust digital infrastructure and banking platforms.

• o Conducted evaluations of IT developments and projects, collaborating closely with IT teams to implement suitable controls by identifying and addressing underlying issues.
• o Communicated the potential vulnerabilities and control deficiencies detected in the audits to upper management, improving my ability to effectively communicate complex technical matters.
• o Generated SQL reports and queries within the database to fulfill the requisites of the Internal Control teams during audit processes.
• o Supported the development and improvement of internal reporting tools and infrastructure to ensure data quality and accessibility for reporting and analysis.

A technology company providing ERP and automation systems solutions for the textile industry, operating both nationwide and internationally.

• Main Responsibilities
• o Developed and maintained comprehensive BI reports using SQL to analyze IoT device data.
• o Engineered a recommendation system using Python and SQL to automate manual fabric sample distribution, utilizing Clustering and Classification for product and customer segmentation
• o Applied data cleaning and processing techniques on large datasets, improving data quality and accuracy for subsequent data mining and machine learning tasks.
• Key Achievements
• o Successfully addressed low order conversion rates by engineering a solution that increased the sample-to-order ratio from 0.98% to 4.92% through precise customer targeting.
• o Examined and automated the installation process of IoT devices, reducing the installation time by 80%
• o Identified KPIs and established frameworks for measuring business performance across different areas, aligning with the need for data-driven decision-making.