Aman Jain

About Aman Jain

I am a Data Scientist at 6sense, a leading account engagement platform that helps B2B marketers and sellers identify, prioritize, and engage with their best-fit accounts. In this role, I create and train state-of-the-art NLP models using transfer learning from existing transformer architectures and fine-tune them for specific use cases, such as sentiment analysis, topic modeling, and information extraction. I also develop automation pipelines and frameworks for measuring the impact and performance of these models. I have a strong background in data science, machine learning, and deep learning, with over three years of work experience in various domains and industries, including government, education, and technology. I have successfully developed and deployed machine learning and deep learning models to solve business challenges and extract actionable insights for key stakeholders using Python, PyTorch, SQL, Tableau, and other tools and frameworks. I have also published a paper on the comparison of BERT and Legal-BERT models for the prediction of Indian legal case judgments, which showcases my research and evaluation skills. I have done my M. Tech in Computer Science with a focus on Data Science, where I have been actively involved in various academic and extracurricular activities, such as being a senator, an executive head, and a core council member of ACM, Association for Computing Machinery. I am passionate and focused on applying machine learning, deep learning, natural language processing, and business intelligence to real-world problems and opportunities. I am always eager to learn new skills and technologies to enhance my knowledge and expertise. I value collaboration, innovation, and impact, and I strive to contribute to the advancement and growth of the data science field and the organizations I work with.

Experience

1. Data Scientist i

   6sense

   May 2023 — Present

   Created a library for training state-of-the-art NLP models which uses transfer learning from the existing transformer architectures and fine tune on a specific use case. • Each of the transformers adds neural network layer(s) at the end, which is then trained on the supplied data.

Education

• Indian Institute of Technology, Delhi

  Master's degree, Data Science

• The LNM Institute of Information Technology

  B.Tech-M.tech Dual, Computer Science