As a Data Engineer at an early-stage climate startup, I am developing and maintaining robust data pipelines, leveraging PostgreSQL to fetch and manipulate large datasets efficiently. I orchestrated complex workflows and scheduled data processing tasks using technologies like Apache Airflow. I created Python scripts for ETL processes, ensuring data quality and integrity. Utilizing AWS, FastAPI, and tools like Kafka and Postman, I deployed data services on virtual machines for seamless integration. Additionally, I actively contributed to database administration using pgAdmin and implemented pandas and numpy for efficient data manipulation and analysis.
Worked in a team and collaborated with Buckinghamshire Council & EY Foundation for a project on the prediction of NEET students. We worked on a tool that predicts School Children at NEET Risk(SCANR). The main purpose of this tool is to generate the list of students who are at high risk of being NEET(Not in Education, Employment or Training) along with their risk scores and provide the report through the PowerBI dashboard.
I worked in a team under the supervision of Prof. Setareh Rafatirad. I worked on supporting an application that analyzes housing data and provides price and rent estimates using machine learning models such as Random Forest and CNN. I collected valuable data and created reports. I also analyzed trends and gave presentations using data analysis to improve the business plan for the application. I also worked on fixing bugs of mobile applications in Android Studio.
Used Photoshop to produce quality content. I was responsible for daily design and frequently added graphic elements.
Wrote tutorials and technical posts on Python, OpenCV, Machine Learning, and many more. You can read some of them here.
Provided KPIs & visualized correlations between compressor speed, fuel flow, carbon emissions, operating hours, power consumed, and nitrogen mole percent in fuel (among other data collected) for 200+ engines, 30+ plants, and 10+ customers using Python and PowerBI.
Presented poster on the prediction of students at NEET(Not in Education, Employment or Training) risk. The Alan Turing Institute exhibited a national collection of DS&AI posters hosted by the University of Warwick and formed part of a competition in which my team won second prize receiving a £100 Love2Shop voucher.
Won the first prize at Hack-Star Bengaluru, which was a hackathon by Mercedes-Benz Research & Development India(MBRDI) in collaboration with Deccan Herald and received INR 2,00,000 in addition to Pre-placement Offer from MBRDI.
Grabbed SanDisk USB and Flashdrive as the prize for "Best Machine Learning Hack" at the Grizzhacks 5 by Oakland University.
Project idea got selected for grand finale at Toycathon'21. Around 1.2 lakh participants from across India registered and submitted more than 17000 ideas for the Toycathon 2021.
Got shortlisted in the Top 15 teams at Eduthon by IIIT Lucknow.
Part of the MLSA program where I connect and learn with students from all over the world and also host and attend technical events.
Mentor in the Machine Learning domain at Innogeeks which is a technical club at KIET. I am also member of the Core Team of Innogeeks.