Sagar Khanna
About Sagar Khanna
Sagar Khanna is a Data Scientist II at TomTom in Pune, Maharashtra, India, with extensive experience in data analysis, automation, and optimization.
Current Role at TomTom
Sagar Khanna is working as a Data Scientist II at TomTom in Pune, Maharashtra, India. His tenure began in 2023, where he plays a crucial role in handling data-driven tasks and developing new analytical models. His responsibilities include implementing machine learning algorithms, optimizing data processes, and contributing to the enhancement of TomTom’s data infrastructure.
Previous Roles at TomTom
Before his current role, Sagar Khanna has had a progressive career at TomTom, starting as a Map Editor in 2017. He subsequently transitioned to roles such as Python/Automation Developer, Data Analyst, Senior Analyst, and Data Scientist I. His cumulative experience at TomTom includes developing automation scripts, performing data analysis, and increasing operational efficiency through innovative solutions.
Education and Expertise
Sagar Khanna obtained his B.Tech in Geoinformatics Engineering from the University of Petroleum and Energy Studies (UPES) from 2013 to 2017. His educational background has provided him with a solid foundation in geo-informatics and related technologies, enabling him to excel in data science roles throughout his career.
Open Source Contributions
Sagar Khanna has actively contributed to open-source projects on platforms such as HuggingFace. He has deployed AI art models and large language model applications, showcasing his expertise in machine learning and artificial intelligence. These contributions reflect his commitment to advancing technology and sharing knowledge with the community.
Project Implementations and Automations
Sagar Khanna has successfully implemented several key projects aimed at enhancing operational efficiency. He implemented a chatbot using GPT-3.5-turbo on Azure OpenAI to handle product release note queries, significantly reducing manual effort. Additionally, he automated NDS maps product statistics validation using Azure Databricks, saving over 30 hours per week for stakeholders. He also optimized data pipelines to reduce cloud expenses by 72%, resulting in monthly savings of €720.