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Hacking Professional Experience

Posted: Thu Feb 08, 2018 4:03 am
by JennyH
Hey guys,

I learned datascience and programming online while working on my marketing job (coursera, udacity). I know want to change my career and take a job in marketing analytics which is way better paid than my marketing agency job.

However, all companies are looking for experienced staff or phds in data science or machine learning. My marketing experience seems to be somehow irrelevant.

My question is: how can I hack the professional experience part? Do you have any experience in doing it?
Should I spend like months pimping a github account or do an extensive marketing research project and put it on github?
Will this help me get the job that requires datascience experience?

I do not want to apply to the jobs and be turned down all the time. So I am looking for a way to hack the process :D

Re: Hacking Professional Experience

Posted: Thu Feb 08, 2018 9:08 am
by FBeyer
You don't HACK something, you DO something. A coveted job position is something you (for the most part) earn your way to. 'hacking' your way there is most likely going to leave you with Impostor Syndrome, earning your way there is going to leave you with a sense of accomplishment.

PhDs know stuff. They know a lot of stuff, but the big question is always whether they actually know how to do something, and if they are going to ask for exorbitant pay compared to other people with a similar skill set. This is exactly where YOU intercalate yourself on the job market. Less pay than a PhD, but you still know how to solve problems.

So when it comes to data science and programming. What can you DO? Can you show, unequivocally that you can solve problems for a company?
What, in your own words, not google's, is data science? What problems do you already know how to solve? Have you had any ideas on how to change the way you do your job, while studying data science.

Let me also ask you a few questions and see if you know an answer, or know how to think about the answers:

1) What do you deem to be an outlier? What are the ways to classify them, and handle them?
2) What are the pros and cons of different ways of dealing with missing data?
3) Why is logistic regression in such widespread use?
4) What is the jacknife, bootstrap, and cross validation used for?
5) L2 norm regularization vs L1 norm regularization; how would you expect the outcome parameters to behave?
6) Do you know dimensionality reduction is? What's the point of dimensionality reduction?
7) What is a weak learner?
8) Describe the basic idea behind ensemble methods
9) What are the assumptions behind linear regression?
10) What is a link function? Can you name some and when to use them?
11) Can you mention some clustering algorithms? What is clustering good for?
12) Do you know the difference between supervised and unsupervised learning?
13) What is data whitening? Why use it?
14) Explain the bias-variance tradeoff?
15) How many of these questions are related to data science?

Re: Hacking Professional Experience

Posted: Thu Feb 08, 2018 9:43 am
by ducknalddon
@jennyH is there any opportunity to do more of this work in your existing job, it's usually much easier to move sideways within an organisation rather than from outside.

Re: Hacking Professional Experience

Posted: Thu Feb 08, 2018 1:56 pm
by FBeyer
... or you could go the perfectly sensible route as DuckNaldon suggested and job-craft your way there... :lol:

Re: Hacking Professional Experience

Posted: Thu Feb 08, 2018 2:44 pm
by jacob
@OP Would suggest not trying to compete with the nerds on their own turf by "hacking". They/we see right through that.

Much better to insert yourself in the intersection between your field and theirs. You understand marketing (which is right next to the money stream, along with sales). Next step is to understand how data science can be used for that [to enhance those money streams]. A crude example (which wasn't crude some years ago) is targeted advertising. To do that you'd need to know ABOUT the capabilities and methods of data science, but you don't necessarily need to know them in detail; although you should know a lot more than just a few buzzwords. Best have built a couple of crude models yourself. (These models are great to show off for interviews. It shows you're not all talk(*).)

This would make it possible for you to bridge to comm gap between the executive team and the nerd team. There aren't many who can do this well, because in order to do so, you have to understand both what you want (on the sales side) and how it might be possible to get it (on the data side). Specialists tend to understand only one of those two.

(*) I can't speak for the executive interviews but having been the interviewer who was supposed to root out the fakers for the technical side, such projects go a loooong way compared to interviewees who think they can handwave their way through the pearly gates or people who think that it's enough to have passed a course.

Good books:

Re: Hacking Professional Experience

Posted: Thu Feb 08, 2018 2:55 pm
by FBeyer
Also, techies don't like talking with business people. The dorks REALLY appreciate having someone with a bit of technical understanding on their side when there is a need to communicate with the business's 'money'.

A good friend of mine, and his single colleague, BOTH have a representative whose sole job is to convey to upper management what those two science dorks dig out of their databases.

Re: Hacking Professional Experience

Posted: Tue Apr 03, 2018 7:25 am
by LookingInward
Sorry to hijack the thread but why do you guys think we divided our labor between technical and business people. Why not have just the technical?

Re: Hacking Professional Experience

Posted: Tue Apr 03, 2018 7:48 am
by daylen

Individual preference?

The skill sets are opposed to each other. Techies reduce things into manageable parts and business people sell the whole.

It also doesn't hurt that the people managing the money are emotional detached from the technical creations.

Re: Hacking Professional Experience

Posted: Tue Apr 03, 2018 3:00 pm
by LookingInward
I was thinking more about the incentives that a company has to reduce costs by having less people. I remember feeling like a fraud at my first job because the programmers did the actual work (and could do mine easily) but I was pushing papers around.

Re: Hacking Professional Experience

Posted: Tue Apr 03, 2018 8:07 pm
by TheProcess
I have been pondering this exact thing for a while. My current role is managerial and I have no technical training except self study, but I'm slightly obsessed with machine learning. We have some PhD data scientists in our department and I would much rather be doing their work. But I'm paid to manage, so I hand off work to my team, then go back to my desk and build GANs lol

If I were super serious about becoming a data scientist (which I'm not necessarily, but looking forward to some of those projects after ERE) I would consider:
-Some kind of bootcamp. Have seen some ones mentioned on r/MachineLearning but can't recall the specific names. It's a quick credential that will open a few doors. When we hire data scientists, we usually get them from the (relatively selective) bootcamps. They usually have multiple offers that they're choosing from.
-Start your own side gig - this skillset is way in demand and someone will be happy for the help. Maybe a pro-bono project? Maybe a friend of a friend owns a business? You'll have to put yourself out there but I do know someone who has picked up gigs this way.

Finally, Jacob's advice is good. I have found that having managerial/strategic insight along with data / coding literacy has been a great place to be. It's not an easy niche to find, but it's good. Ways to get there would be to (1) find a headhunter who specializes in this sort of thing or (2) start contributing to, or just straight up initializing quantitative projects in your existing job.

Re: Hacking Professional Experience

Posted: Tue Apr 03, 2018 11:05 pm
by SavingWithBabies
@JennyH Have you thought about relocating to the SF Bay Area? It's got to be the easiest place to level up your job titles. I didn't do it there but I observed many people doing it and still do today. Startups are willing to take more of a risk and are almost always looking for talent (unless the investment money stops flowing). My understanding is over the past couple years, machine learning work/data science has started to filter down to regular developers without advanced degrees.