Tech IndustryJul 13, 2019
Visayzc

Which companies have real data scientists?

I have a MS in DS from top tier CS university, and I have been working as a data scientist for less than 1 year. Daily work includes analytics & AB testing (60%) and building ML models (40%). The current team has some modeling projects, but the larger organization is mainly doing analytics. Their daily work is just SQL+story telling. I would like to transition to a role that is more modeling focused, and has better engineering culture in the next year. Can anyone give me some suggestions on which companies/roles I can apply for if I want to avoid SQL everyday roles? I mean I want to be real data scientist instead of SQL scientist which is in essence business analyst. Some potential roles are as follows: Google: Quantitative analyst, Software engineer in highly ML team FB: DS analytics, DS infrastructure, SWE in highly ML team LinkedIn: DS, MLE Amazon: DS, Research scientist, Applied scientist Apple: DS, MLE Uber: DS Lyft: DS Airbnb: DS analytics/inference/algorithm track Netflix: DS Pinterest: DS, MLE Twitter: DS, MLE Quora: DS Any suggestion will be appreciated.

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Quora hpsB38 Jul 13, 2019

Quora DS, MLE

Visa yzc OP Jul 13, 2019

Thanks. Is the DS in Quora more focused on AB testing?

Quora hpsB38 Jul 17, 2019

Yes

PayPal thtP05 Jul 13, 2019

Checkout the PayPal GRDS team they only hire Ms and PHD's. Work is 100% modelling for some teams

Mu Sigma ERhl51 Jul 13, 2019

I'll give my 2 cents: 1. Rather than focus on the company try finding out exactly what the data science you apply to does. It is completely possible that you end up doing the same work in a new employer. 2. Try to find roles in engg/technical teams rather rather than business/ops teams. 3. Please decide what you mean modelling focused : are you interested in developing models or are you interested in putting models into production (2 are every different beasts). For the former, most companies prefer PhDs with a heavy stats focus. Your CS background might be more suited to deploying models into production. I would also encourage you think about your work in terms of interesting content vs interesting outcomes. Sounds like you are more inclined towards work with interesting content. You should think about all this before making a switch. PS: learn SQL properly, it's a lot of fun once you master it :)

Visa yzc OP Jul 13, 2019

Thanks for the insights. Interesting content vs outcome is a good point! Btw I already master SQL well, it’s just I don’t like writing it all day. There is no algorithmic thinking in it.

Mu Sigma ERhl51 Jul 13, 2019

Haha. It can get quite algorithmic as the size of the ADS increases and the data is not well curated (which might be rare in VISA). A piece of unsolicited advice, sounds like you don't enjoy story telling because you aren't good at it. Practice story telling better, writing SQL will be more fun and you'll enjoy your current job a lot more. Try to become a full stack SWE who can do modelling, IMHO they will command the highest market rate.

LinkedIn ex-fb Jul 13, 2019

Look at the pay and you’ll know what are ML engineer roles and what are BA roles

Visa yzc OP Jul 13, 2019

For ML engineers, what percentage of time is spent on modeling and general engineering work?

Amazon snostreblA Jul 13, 2019

Avoid DS and quantitative researcher at Facebook and Google. Those are essentially SQL monkey/BI analyst roles. Research scientist or machine learning scientist/engineer means you get interesting work.

Visa yzc OP Jul 13, 2019

What’s the bar for ML engineers at Google and FB? Is there any requirement on year of experiences?

Micron ChaChingb Jul 13, 2019

I feel DS is a very generic one size fits all role. If you're interested in ML/DL type of roles search for Applied Scientist/Deep Learning Engineer/Research Scientist from top tier firms. Company product portfolio is very important, FB/Amazon Applied Scientist/Research Scientist roles are heavy into DL modelling whereas their DS roles are BA+simple ML. Nvidia does a lot of ML/DL with Vision work with AS/RS roles.

Chegg snt124 Jul 13, 2019

1) just read job description. Most ml roles will emphasize expertise in ml, while most sql monkey roles might say like “knowledge in ml” or not mention ml at all 2) any ds role with “analytics” in the title is likely non-ml heavy 3) if a company has roles like research scientist, machine learning engineer, etc., then likely the “data scientist” title is more analytics and those other roles are ml heavy

Micron ChaChingb Jul 13, 2019

Good eye. But quite a lot of companies use generic job description when opening a job role. From a company's perspective using generic role would, 1. Save time - HRs don't bother Managers and work with what they have. This becomes a lot easier if you are FANG. Reputation brings resumes through the door, hardly the JD. 2. Projects are not well defined, budget closes soon. Generic description works and once the candidate is in the door we can decide what they do, could be ML research or BA type role.

Chegg snt124 Jul 13, 2019

Yeah I mean there’s no silver bullet answer; these things will help. And ultimately conversations with the hiring manager will provide the most clarity

LinkedIn fisheixnd Jul 13, 2019

With title inflation being what it is, draw your own conclusions on what data science really is.

New
dBBO43 Jul 13, 2019

Openai, Google brain

Axtria DesiLaunda Jul 13, 2019

How to get there?

Axtria DesiLaunda Jul 13, 2019

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