Prashant Sinha Email & Phone Number

Area of expertiseData-warehousing, Business Intelligence, Data modelling, Data Science &Analytics, Could Architect, Data Migration ,Performance Tuning, Digital Transformation • Leading teams supporting clients on diverse Analytics projects.• Expertise in building large-scale decision support system and serving as an expert in Data strategic projects.• Expertise in setting up Data Engineering and analytics capability in organization.• Functional knowledge of Data Analytics, Process Mining and Artificial Intelligence• Expertise in designing application with cutting edge technology like azure Kubernetics, EventHub and advanced Machine learning algorithms.• Had wide range of industry experience in Development, Architecting & Delivery of ETL/Reporting, Migration and Analytical projects.• Expertise in making strategic recommendations to build reliable, scalable, and high available system.• Working with business and SME to identify potential areas of improvement in current system.• Efficient in integrating state-of-the-art Big Data technologies with traditional DWH architecture.• Expert in providing the solution to Business and help identifying right set of tools /technology for implementation.• Expertise in the development of regression, classification,and various model in the field of Data science.• Experience in various data science technique, GenAI, Computer Vision and Model diagnosis.• Experience in the infra as code using terraform, ARM template and establishing the ADO pipeline.• Expertise with Fast API, Docker based deployment into Azure cloud, Load lancer, cluster IP etc.Major Project• Automated Document Processing is a state-of-the-art, cloud-based self-serve data extraction platform. It is a unique artificial intelligence platform that can automate the extraction of meaningful data from various types of documents – varying in templates and file formats. The extracted data is processed, then shown to the business users in a user interface for self-service features, for quick manual review, amend information if needed and submit. Built on a scalable platform, SKENSE leverages technology, analytics, and deep domain expertise to provide productivity benefits to business users and clients in the process of extracting data from unstructured and semi-structured documents.• A Data Warehouse project to consolidated data from various sources at centralized location to support their reporting as well as analytics requirement.

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  Bit Sindri

  Computer Science