Abstract: The media's recent portrayal of the data science career path as an extremely attractive career option can appear very enticing. However, for those who have dedicated a large part of their working life towards traditional career paths that are now no longer inspiring or motivating, making this transition to become a data scientist can seem like a very daunting endeavor if not a pipe dream. This talk is to describe in hyper realistic terms what to expect during this transition and learn techniques that may help, from someone who has been through this transition.
Bio: Sri Kanajan is currently a senior data scientist at Uber working on causal inference techniques to increase the effectiveness of promotions both by experimenting different promotion strategies as well as targeting users using causal inference based machine learning. Prior to that he was a senior data scientist at Goldman Sachs working on detecting market manipulation in HFT. He transitioned from scratch into a data science career after over 15 years as a systems engineer and manager in the automotive and medical device sector by retraining through a bootcamp, continued focus on building out key skills and a solid portfolio. He also served as the lead instructor at General Assembly's part time evening data science class for over 2 years.