• Developed the Python backend logic for a Digital Twin of an Oil & Gas plant used for simulation and optimization
• Developed a Python code to fetch data from multiple sources and generate a report in MS Excel
• Designed the architecture for the digitization application of the Oil and Gas plant
• Deployed Python codes using docker containers
• Developed backend code for an API to send data for a dashboard from the database

• Customer segmentation using a clustering model to segment customers for marketing campaigns
• Analyze marketing data with Python and share insights with the marketers for measuring campaign performance
• Building performance measurement dashboards for marketing campaigns for analyzing the target audience
• Build performance measurement dashboards for marketing campaigns
• Develop the technical flow/architecture and code for complex marketing campaigns using Python & SQL
• Process enhancements by applying automation to minimize the data latency and achieve seamless data integration
• Create a data pipeline to migrate data from GCP to the Teradata database using Python and BigQuery
• Understand business processes, the flow of data within and between processes, and recommend process improvements for better data quality and effectiveness

• Set up docker containers on Nvidia DGX server for training deep learning models and enable rapid analytics using GPUs
• Created a dashboard to monitor server and GPU statistics using NVDashboard and deployed using Docker
• Developed a Flask Application to manage ML models and provide an admin console for NVIDIA edge devices
• Developed Python scripts for converting raw data into KITTY Format for object detection algorithms
• Enabled rapid data analytics on huge datasets using Nvidia GPU by setting up docker containers on server
• Created visualizations using Tableau Desktop and Python libraries (Plotly, Seaborn, Matplotlib)
• Schedule python scripts on Linux Servers and devices (NVIDIA Jetson Devices, NVIDIA DGX Server)
• Used transfer learning to re-train and deploy pre-trained ML models from Nvidia AI for License Plate Recognition

Data Analysis

• Developed Python scripts for cleaning and formatting data received from inbound systems
• Developed monthly sales reports in Tableau Desktop

Application Development

• Developed PLSQL procedures and functions for data processing as per the project requirement
• Scheduled shell scripts using Crontab and handled Oracle Solaris Servers based on Linux
• Performance Tuning of the database procedures and SQL queries using profiler and index rebuild techniques
• Improved performance of application via Index Rebuild and gained 18gb space on the Production server
• Used SQL Loader to load data from flat files to Oracle tables
• Developed the front-end for an Oracle Data Migration web-based tool using HTML, CSS and JavaScript

International Institute of Information Technology - Bangalore (IIIT-B)

Gained Practical Knowledge and Skills in data science by working on industry projects. Trained by IIIT-B professors and industry experts to follow best practices in accumulating data, manipulating it, and gaining insights. Learnt about tools such as Python, R Programming, MySQL, Tableau. Worked on multiple projects of Data Analytics and Machine Learning.

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College of Engineering Roorkee

Presented a Research Paper on Designing and analysis of digital circuits using CNTFET in a National Conference on Research and Innovations in Engineering and Technology in association with Institution of Engineers, India
Member of the robotics club. Created a self-balancing robot using embedded electronics.
Winner of annual coding challenge conducted by the technical committee.
Coordinated and managed multiple events in the cultural committee.

Capgemini India

Awarded with the Project Star Award for excellence in customer delivery and outstanding performance in Oracle Apps NA Business Unit At Capgemini.

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Nvidia Deep Learning Institute

RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows, and together with Dask, can leverage multiple GPUs on larger-than-memory datasets.

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