Emmanuel Adeitan Email and Phone Number

● 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

• Managing master data, including creation, updates, and deletion.• Provide quality assurance of imported data, working with quality assurance analysts if necessary.• Supporting the data warehouse in identifying and revising reporting requirements.• Helping develop reports and analysis.• Commissioning and decommissioning of data sets.• Managing and designing the reporting environment, including data sources.• Managing users and user roles.• Processing confidential data and information according to guidelines.

● Designed machine learning models for real-time weather prediction and deployed themusing AWS SageMaker.● Built data pipelines using Apache Kafka for streaming large-scale datasets into AWSRedshift.● Key Tools: AWS SageMaker, Apache Kafka, Python, Docker.Projects:1. Built a distributed data pipeline to ingest real-time weather data for a prediction model,optimizing storage and processing with AWS S3.2. Deployed scalable models on AWS SageMaker using Docker containers forreproducibility.

• Managing master data, including creation, updates, and deletion.• Provide quality assurance of imported data, working with quality assurance analysts if necessary.• Supporting the data warehouse in identifying and revising reporting requirements.• Helping develop reports and analysis.• Commissioning and decommissioning of data sets.• Managing and designing the reporting environment, including data sources.• Managing users and user roles.• Processing confidential data and information according to guidelines.