Data Scientist | Mlops Engineer
Current● Machine Learning Development: Designing and implementing predictive models to optimize various aspects, such as customer segmentation, recommendation systems, and sales forecasting.● Data Engineering: Leading the migration of our company's data to a new, more efficient database system, ensuring data integrity, security, and accessibility.● Data Pipeline Optimization: Developing and maintaining ETL (Extract, Transform, Load) processes to streamline data workflows and improve data quality.● Collaboration: Working closely with cross-functional teams to identify business needs and translate them into technical solutions, contributing to strategic decision-making.● Performance Monitoring: Implementing and monitoring machine learning models in production environments, analyzing their performance, and iterating to improve accuracy and efficiency.● Developed and deployed machine learning models using Azure ML pipelines tooptimize customer recommendations.● Built end-to-end ML pipelines using Azure DevOps and CI/CD automation for real-timemodel deployment.● Led model optimization efforts, reducing deployment time by 30% and ensuringseamless integration into production systems.● Key Tools: Azure ML, Python, SQL, Docker, GitHub Actions, Azure Data Factory.Projects:1. Developed an ETL pipeline using Azure Data Factory to streamline customer dataprocessing for a recommender system.2. Implemented CI/CD pipelines on Azure DevOps to automate model testing anddeployment