Sourav Chattaraj
About Sourav Chattaraj
Sourav Chattaraj is a Senior Data Scientist at Securly, with a strong background in mathematics and computer science. He has diverse experience in data science and coaching, having worked in various cities in India and contributed to multiple projects involving advanced neural network techniques.
Work at Securly
Sourav Chattaraj has been employed at Securly as a Senior Data Scientist since 2021. In this role, he applies his expertise in data science to develop solutions that enhance the company's offerings. His work involves tackling complex data problems, leveraging his strong foundation in mathematics and computer science. He continues to contribute to Securly's mission while based in Pune.
Education and Expertise
Sourav Chattaraj earned an Integrated MSc in Mathematics and Computer Science from Birla Institute of Technology, Mesra, completing his studies from 2012 to 2017. He further expanded his knowledge in deep learning through a course offered by NPTEL, focusing on neural networks, convolutional neural networks, recurrent neural networks, and autoencoders, which he completed in 2020.
Background
Sourav Chattaraj has a diverse professional background, having worked in various cities across India, including Mumbai, Pune, and Kolkata. His career includes roles at Morningstar as a Data Scientist and at Quantiphi, Inc. as a Machine Learning Engineer. He also gained research experience as a Research Trainee at the Indian Statistical Institute in Kolkata.
Coaching Experience at upGrad
Since 2021, Sourav Chattaraj has served as a Coach at upGrad while maintaining his position at Securly. In this dual role, he utilizes his data science knowledge to mentor students, helping them develop skills in the field. His experience in both educational and professional settings enhances his effectiveness as a coach.
Contributions to Data Science Projects
Sourav Chattaraj has contributed to various data science projects that involve advanced techniques such as neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. His work in these areas demonstrates his ability to address complex challenges in data science and contributes to the advancement of the field.