Vennela Seelam
About Vennela Seelam
Vennela Seelam is a Data Scientist and Research Assistant currently working at Sia Partners in Paris, France. She has a background in Electrical, Electronics and Communications Engineering and has experience in software development and data science, including projects involving natural language processing and sentiment analysis.
Work at Sia Partners
Currently, Vennela Seelam serves as a Data Scientist - Research Assistant at Sia Partners in Paris, Île-de-France, France. She has been in this role since 2024. In her position, she focuses on implementing reverse engineering methods on texts produced by generative models. This work aims to verify data authenticity and maintain high-quality standards in dataset creation.
Previous Experience at Cognizant
Vennela Seelam worked as a Software Developer at Cognizant from 2021 to 2023 in Chennai, Tamil Nadu, India. During her two years at the company, she gained experience in software development, contributing to various projects and enhancing her technical skills in the field.
Internship Experience
Vennela completed an internship at Vishakhapatnam Port Trust from 2019 to 2020, where she worked on-site for one month. Additionally, she interned at McDermott International, Ltd for two months in 2019 in Chennai, Tamil Nadu, India. These internships provided her with practical experience in engineering and project management.
Education and Expertise
Vennela Seelam holds a Bachelor's degree in Electrical, Electronics and Communications Engineering from Andhra University, which she completed from 2017 to 2021. She furthered her education by obtaining a Master's degree in Data Science & Business Analytics from ESSEC | CentraleSupélec. This educational background equips her with a strong foundation in both engineering and data science.
Research and Projects
In her academic pursuits, Vennela has worked on a corporate research project in collaboration with Essec Business School and CentraleSupélec. This project focused on natural language processing and sentiment analysis. She has also utilized OpenAI GPT 3.5 for categorizing extensive text datasets and applied BERT for sentiment analysis to quantify market trends.