Webinar: Data Science in Tech | Coursera Community
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Webinar: Data Science in Tech

Webinar: Data Science in Tech
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Join Data Science Manager at Instagram JB Sibille to learn about the role of data science in analytics at tech companies. Learn about key differences between the role of a data scientist and other roles as well as what can make a data scientist successful. JB takes your questions about the type of work data scientists do and how they collaborate with other functions to have impact.

This webinar took place on 29 March 2019. Presentation slides are attached.

Webinar Recording
https://youtu.be/2JfEaV5h1MY

About the Speaker
JB Sibille grew up in France and went to grad school in California for systems engineering. While his background was more on the theoretical side of Math and Physics, he learned about the potential applications of these skills by taking Andrew Ng's Machine Learning course on Coursera. After diving deeper into the field during his master's, JB started working at Facebook 4.5 years ago, and he has worked on two different products there: Facebook Events and Instagram Feed & Stories.

10 replies

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@achagnou, @HarshadD – you might be interested in this webinar!
Thank you @Laura for setting this up. Looking forward to the webinar this Friday!
I'll be covering topics ranging from how I got into data science to some core differences between the different roles in data science to what makes an effective data scientist in tech.
The webinar will be oriented towards people who are thinking of pursuing a career in data science or trying to build up a data science organization. I tried to think about what were the questions I had in my mind before starting down that path and my current take on the answers.
Thanks to the organizer and the guest of this webinar! I'm a fellow Coursera learner since 2013. I'm one of the early students who took the ML course, Data Science Specialization, deeplearning.ai courses and many more. I really appreciate that I could learn all this knowledge from the best with students all over the world on such a wonderful platform. Much like JB, I also have a physics background and became a ML enthusiast since grad school. Last year I interviewed at Instagram@NYC for a machine learning engineer position but did not get it unfortunately. The interview was mostly about ranking algorithm, NLP related questions and of course, Leetcode style questions. I guess I didn't prepare well enough :)

My main question is about the definition of data science and data scientist in your opinion.

  1. What are the main types of data scientists/ML engineers at Instagram?
  2. What are the types of data scientists in the more general data science world in your opinion?
  3. If I would like to work on the application of an aspect of deep learning, am I a data scientist, a ML engineer, or a research scientist? What are the differences if they all exist in the same team? (I know these titles exist in FB in particular)
  4. What do you look for as a hiring manager?
Data science is growing into such a huge field in the industry now. It means different things in different companies, even teams. For example, Uber has entire organization of data scientists from operations research and economics. I also heard from DS friends who complained about being "SQL monkey" all the time. Many of these people have the career goal of working in production ML systems, but sometimes they are in an organization where those are worked on by engineers.

I also observed that DS from fields like OR or economics usually don't have CS/software engineering skills, such as writing application/tests, or even using Git. For sure these can be developed over time, but it depends on what an organization expects from its data scientists. I have met a few brilliant all-star data scientists who are not only expert in machine learning, statistics, causal inference, but also know all about software engineering. And I have also met data scientists who don't know how to use github, and are not willing to learn how to write unit tests. People generally have consistent expectation for other titles such as front-end or back-end engineers. If we treat "data scientist" as a concept on the same level as "engineer", it never seemed to have a clear taxonomy, or a set of consistent job description, or career path to me.

Sorry if this is a big question. Just wanna learn how you think about these things. Thanks!
Questions for seminar.

a) When looking at diversity and inclusion for both hiring and working practices in your field how do you ensure you have diversity in your teams and what inclusion intitiatives do you operate ?

b) How do you think this contributes towards your teams work in data science?

c) Do you proactivly source candidates from underrepresented communities or groups?

d) Would you be willing to share/comment upon the profile of your teams in an aggregate sense EG: age groups , gender , education levels , background , orientation etc .
Hi Jean Baptiste! The two topics that you will cover are really interesting.

I studied AI 20 years ago at UCL London. I liked a lot but it was too early.
I have taken also Andrew NG machine learning and deeplearning courses to update my knowledge.

Because I have a more senior profile, I see myself more as Data Science Manager like your actual job.
What do you think are the requirements for a Data Science Manager position and what work do you do there?

Thanks and all the best!
Hi,

As an aspiring data scientist I can learn data analysis methods and techniques my self. But how can I learn domain skills?

Thanks to the organizers for arranging this webinar. I may not be able to attend due to my time zone, but I will watch the recording later.

Best regards,
Thileepan
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Thank you for all your thoughtful questions so far! And yes, @Thileepan, the recording will be available. It's hard to find a time that works well for everyone – thanks for your understanding!
Thank you for this webinar! Could you tell us how you feel about data science bootcamps vs. a proper 5 year bachelor + master degree? Do you really learn how to think like a data scientist in the data science bootcamps?
Sorry that I couldn't attend the webinar due to Time Zone differences.
Please provide me the link of the webinar recording, I really want to look into it.

(Moved to this thread by @Laura)
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Hi @gadia-aayush. I will be posting the webinar recording here soon. If you signed up for the webinar (even though you didn't attend), you will also get an email from me within a few days with a link to the recording. Thank you!

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