Ziad ahmed Abdelsalam

Data AnalystFull Time

• Job Details:Designed and optimized data pipelines and ETL processes to ensure efficient data ingestion, processing, and integration. •Implemented data warehousing solutions to centralize and organize diverse data sources, enabling efficient data storage, retrieval, and analysis for informed decision- making. •Developed and implemented machine learning models for customer churn prediction, resulting in an 8% reduction in churn rate and the retention of 141 merchants. •Developed and Deployed a NPS model to smart tag customers based on their nps comments and identify customer experience issues. •Utilized advanced statistical techniques and time series analysis to forecast the number of acquired merchants, enabling effective resource allocation and growth planning. •Collaborated with cross-functional teams to identify business opportunities, develop machine learning solutions, and deliver actionable insights. •Developed technical documentation for different machine learning models. •Deployed Machine Learning Models in Data pipelines. •Utilized tools such as Tableau, Python, Talend, and SQL for data analysis, visualization, and ML model development.

Machine Learning Engineer -RemotePart Time

• Job Details:Developed and deployed machine learning models for customer segmentation, churn prediction, recommender systems, and CLTV prediction, resulting in improved customer targeting, reduced churn, and increased revenue. These models were containerized using Docker and seamlessly deployed on a website, allowing for efficient scalability and easy deployment across different environments. •Created fully integrated connectors for Woocommerce APIs and SQL databases, facilitating seamless data integration and automation processes. These connectors were utilized to extract data from Woocommerce APIs and SQL databases, which were then processed and used to train the machine learning models. The models, once trained and containerized using Docker, were deployed on a website to provide real- time predictions and recommendations to users. •Implemented machine learning models using Docker containers for scalability and easy deployment across different environments. By containerizing the models using Docker, they became self-contained and portable, allowing for seamless deployment on various platforms and environments, including the website. This approach ensured that the models could be easily scaled up or down based on demand, providing efficient resource utilization. •Utilized Python, Visual Studio Code, Postman, and JSON for model development and integration. I leveraged Python as the primary programming language for model development and utilized Visual Studio Code as the integrated development environment. Postman was used to test and integrate the APIs, such as Woocommerce, for seamless data retrieval. JSON was employed as the data interchange format to ensure smooth communication between different components of the system, including the website and the machine learning models deployed in Docker containers.