Akash Brahmey

Spearheaded the seamless transformation of data from MongoDB to Redshift, resulting in optimized data storage. Employed KNN algorithm to accurately impute missing values, significantly improving data completeness. Perform comprehensive customer segmentation using advanced clustering algorithms. Develop and ensure the smooth operation of the ETL pipeline, facilitating uninterrupted dataflow. Implement stringent monitoring and governance protocols to guarantee data integrity. Developed advanced predictive models for container ballast days, optimizing logistics and enhancing operational efficiency. Utilized the IMOS database to improve model accuracy, resulting in streamlined container management. Successfully forecasted CO2 emissions for voyages, aiding in sustainability initiatives and environmental responsibility. Collaborated cross-functionally to seamlessly integrate predictive models into the data zone. Enabled real-time access to predictions, facilitating data-driven decision-making across the organization and promoting operational agility.

Led data governance and monitoring efforts for a global FMCG client, ensuring data integrity and compliance. Crafted KPI dashboards using Power BI to provide real-time insights for informed decision-making. Conducted rigorous quality checks on incoming data from diverse global sources. Orchestrated data integration from various datasets to streamline the processing pipeline. Utilized Python for automated data processing of Excel files, including data cleaning and calculation of various factors after combining datasets. Ensured consistent data quality throughout the processing cycle, delivering high-quality data to clients.

Headed a team of 4 in the realm of predictive maintenance and fault resolution for industrial machines. Applied machine learning algorithms to historical data, predicting maintenance needs and significantly reducing downtime and operational disruptions. Diagnosed and resolved machine faults through a combination of historical data analysis and hands-on manual intervention. Monitored machine conditions to ensure optimal performance and operational efficiency. Designed and implemented dashboards, leveraging tools like Power BI for real-time KPI monitoring of machines. Played a pivotal role in scheduling and executing timely maintenance activities, guaranteeing continuous machine functionality. Contributed significantly to a seamless production process by proactively addressing machine-related issues, optimizing operations, and improving overall efficiency.

Acquired hands-on experience in predictive maintenance and fault resolution for industrial machines. Played a key role in applying machine learning algorithms to historical data, contributing to the prediction of maintenance needs and reducing downtime and operational disruptions. Collaborated closely with senior team members in diagnosing and resolving machine faults, employing a combination of data analysis and manual intervention. Actively participated in the design and implementation of dashboards for real-time KPI monitoring of machines, utilizing tools like Power BI.