Ersan Kucukoglu

Key Achievements Automated corporate card checks using Python, SQL, and Excel,reducing manual workload by 98%. The process helped identify active cards to prevent ongoing charges, fraud transactions, and potential misuse, particularly during employee terminations. Received the Q1 2024 Employee Excellence Award for outstanding contributions to process optimization and innovation Provided end-user support through ServiceNow, achieving a 97% incident quality rate, 91% SLA compliance, and a 4.8/5 CSAT score.

Collaborated with cross-functional teams to conduct data analysis and visualization, working closely with key stakeholders to drive data-informed decision-making Executed deep data analysis and data cleaning using SQL, Excel, Python, and Snowflake, ensuring data accuracy and reliability for critical business insights Developed and maintained interactive dashboards in Tableau, Looker, and ServiceNow, which streamlined data visualization processes and empowered stakeholders with actionable insights and KPIs

• Built Python-based Databricks data product for monthly plastic packaging weight prediction and tracking using advanced time series analysis. • Utilized SQL, Python (including pandas, numpy, PySpark, and plotly) for data cleaning and exploration. • Implemented forecasting models, achieved 64% improvement in the model performance. • Developed SQL Dashboard for analyzing and predicting sales forecasts, enabling optimized plastic packaging decisions.

• Designed offer and cost analysis for projects with procurement, design, and manufacturing collaboration. • Streamlined the process of creating offers and designs for clients by developing a customized solution in Excel, reduction errors and saved 45% time • Created monthly sales and customer reports, uncovering insights to drive business growth and enhance customer satisfaction. • Built monthly sales and customer reports, revealing actionable insights for business growth and customer satisfaction .

Researched data science projects at the Cambridge Center for Data Driven Discovery, participated in a Machine Learning Clinic Session, and attended a statistics course in the Computer Science Department.

Courses: Coding: Data Management and Analysis with R, and Python; Data Analysis (EDA, Prediction, Time Series, Machine Learning); Data Engineering: SQL, Cloud Computing (AWS), Big Data Computing with Apache Spark, Data Science: Machine Learning Concepts, Data Visualization with Tableau, Python Programming, Visualization and Text Analysis, Agile Project Management and Scrum • Full scholarship recipient, demonstrated exceptional academic merit

• Courses: Computer Programming, Transform Techniques with Computer Applications, Basic Information Technologies, Statistics, Microprocessors, Wireless Communications • Received scholarship from Roma Education Fund and Senbas Group