How to Build a strong Data Science Resume. With Chris Deotte, Quadruple Kaggle Grandmaster at NVIDIA - What's AI Podcast Episode 2 What's AI Podcast by Louis-François Bouchard

An interview with one of the best Kaggler out there, Chris Deotte. Chris is a Senior Data Scientist at NVIDIA and is getting paid for his Kaggle skills! Kaggle is a platform mainly known for hosting machine learning competitions...



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Chris's GTC events: 

►Developing State-of-the-Art Models in a Short Time:  https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666650462301001Ltpf

►Learn How to Create Features from Tabular Data and Accelerate your Data Science Pipeline:  https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666168670726001zds5



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Chapters:

00:49 What is your academic background? 

01:20 How did you get into data science from a mathematics background? 

02:04 What is a data scientist for you, and what is your role as one? 

02:33 Do you think data science is mainly a role for academia because it’s a lot of statistical and math knowledge? Do you think a PHD or a  masters is necessary to get such a role? 

03:47 What is your role as a data scientist at Nvidia? 

05:40 What is Kaggle, and what is a grand master at Kaggle? 

08:20 Do you think Kaggle competitions are a good way of improving your resume and build experience if you want to work in the industry? 

11:54 Is there something specific to Kaggle that doesn't work in the real world? 

16:29 Are most competitions similar to one another? Or are there different challenges depending on the competition? 

18:34 So Kaggle will allow you to be a generalist? 

19:08 What tips would you give to a beginner who wants to participate in the competition and have a chance of winning? 

20:43 Do you participate in competitions of every field? 

24:17 What is a Kaggle grandmaster and what does it mean to have this four times? 

27:52 Was there a category that was harder for you? Or one that you didn't enjoy? 

30:38 What was the main factor for Nvidia to find you and hire you? 

32:11 How was the interview process if they already knew how you worked and your knowledge? 

35:07 How did you prepare for these interviews? 

36:28 How can they assess your skills if there are so few people that do what you do? 

37:27 Since the technical interviews are in different fields, is it over if you fail one of them? 

40:04 Can you describe your day to day at Nvidia? 

41:29 So you're being paid to do what you love to do? 

43:03 Could you enter into the details of a recent project? 

46:10 How do you deal with a very large data set? 

48:39 Do you have a machine or are you connected to servers? 

49:56 What would you recommend to someone who has a basic laptop and wants to practice DS? 

53:37 Do you sometimes need to do particular processes to make it work with multiple GPU's? 

56:39 What are the daily tools you use to do data science and Kaggle? 

58:00 Is there anything we can learn from Nvidia coming soon? 

58:58 Is it accessible for someone just starting at Kaggle?