About

Bhavesh Thakkar is a highly skilled Quantitative Researcher with a passion for System Design, Product Management, and Entrepreneurship. He is currently working at a High Frequency Trading firm, where he applies his expertise in Machine Learning and Deep Learning to develop trading strategies. Bhavesh has a strong academic background, having pursued a Bachelor's (Hons.) degree in Mechanical Engineering from the prestigious Indian Institute of Technology, Bombay, with a minor in Computer Science. His academic achievements are impressive, with a GPA of 9.14/10 in his major and 9.0/10 in his minor. Before joining the HFT firm, Bhavesh worked as a Machine Learning Engineer at Sprinklr, where he used NLP-based Deep Learning techniques to extract information from unstructured queries. He also interned at Microsoft Research, India, where he worked with the Systems Group on large-scale training of Recurrent Neural Networks (RNNs). During his internship, he parallelized architectures like Seq2Seq, Word2Vec, and RNN-based Classifiers in PyTorch and TensorFlow and studied the effects on accuracy and training time upon scaling them to large batch sizes. Bhavesh is a Research Scientist with a wealth of experience in the field of Deep Learning. He has expertise in NLP, RNN, Word2Vec, TensorFlow, PyTorch, Neural Networks, and other related technologies. He is also interested in Product Management and Entrepreneurship, which he sees as natural extensions of his skills and interests. Outside of work, Bhavesh enjoys playing chess and reading non-fiction. He is a highly motivated individual who is always looking for new challenges and opportunities to learn and grow. With over 4 years of relevant experience, Bhavesh is a valuable asset to any team looking for a skilled and passionate Quantitative Researcher.