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Episode Info: Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.  In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.  Enjoy the show! We speak about: [01:50] How Pavel started in the data space  [09:50] Vietnam is an ideal space for working remotely and teaching English  [21:40] The moment Pavel found Kaggle  [24:20] How Pavel became a data scientist  [28:00] Difference between machine learning engineers and researcher data scientists  [31:50] Why is it essential to be the very best? [34:20] Machine learning and mathematics  [36:45] The early days of Pavel’s Kaggle journey  [40:00] Pavel’s favorite part of Kaggle  [47:20] The role of automation in Kaggle    [49:40] The steps when approaching a new Kaggle competition  [52:55] Think twice before you commit to data science  Resources: Pavel’s LinkedIn: Pavel’s Kaggle: Pavel’s Twitter: Now you can support Data Futurology on Patreon!  Thank you to our sponsors:  UNSW Master of Data Science Online:  Datasource Services: or email Will Howard on  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message:
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