Home > data science, finance, musing, statistics > How to reinvent yourself, nerd version

How to reinvent yourself, nerd version

April 22, 2013

I wanted to give this advice today just in case it’s useful to someone. It’s basically the way I went about reinventing myself from being a quant in finance to being a data scientist in the tech scene.

In other words, many of the same skills but not all, and many of the same job description elements but not all.

The truth is, I didn’t even know the term “data scientist” when I started my job hunt, so for that reason I think it’s possibly good and useful advice: if you follow it, you may end up getting a great job you don’t even know exists right now.

Also, I used this advice yesterday on my friend who is trying to reinvent himself, and he seemed to find it useful, although time will tell how much – let’s see if he gets a new job soon!

Here goes.

  • Write a list of things you like about jobs: learning technical stuff, managing people, whatever floats your boat.
  • Next, write a list of things you don’t like: being secretive, no vacation, office politics, whatever. Some people hate working with “dumb people” but some people can’t stand “arrogant people”. It makes a huge difference actually.
  • Next, write a list of skills you have: python, basic statistics, math, managing teams, smelling a bad deal, stuff like that. This is probably the most important list, so spend some serious time on it.
  • Finally, write a list of skills you don’t have that you wish you did: hadoop, knowing when to stop talking, stuff like that.

Once you have your lists, start going through LinkedIn by cross-searching for your preferred city and a keyword from one of your lists (probably the “skills you have” list).

Every time you find a job that you think you’d like to have, take note of what skills it lists that you don’t have, the name of the company, and your guess on a scale of 1-10 of how much you’d like the job into a spreadsheet or at least a file. This last part is where you use the “stuff I like” and “stuff I don’t like” lists.

And when you’ve done this for a long time, like you made it your job for a few hours a day for at least a few weeks, then do some wordcounts on this file, preferably using a command line script to add to the nerdiness, to see which skills you’d need to get which jobs you’d really like.

Note LinkedIn is not an oracle: it doesn’t have every job in the world (although it might have most jobs you could ever get), and the descriptions aren’t always accurate.

For example, I think companies often need managers of software engineers, but they never advertise for managers of software engineers. They advertise for software engineers, and then let them manage if they have the ability to, and sometimes even if they don’t. But even in that case I think it makes sense: engineers don’t want to be managed by someone they think isn’t technical, and the best way to get someone who is definitely technical is just to get another engineer.

In other words, sometimes the “job requirements” data on LInkedIn dirty, but it’s still useful. And thank god for LinkedIn.

Next, make sure your LinkedIn profile is up-to-date and accurate, and that your ex-coworkers have written letters for you and endorsed you for your skills.

Finally, buy a book or two to learn the new skills you’ve decided to acquire based on your research. I remember bringing a book on Bayesian statistics to my interview for a data scientist. I wasn’t all the way through the book, and my boss didn’t even know enough to interview me on that subject, but it didn’t hurt him to see that I was independently learning stuff because I thought it would be useful, and it didn’t hurt to be on top of that stuff when I started my new job.

What I like about this is that it looks for jobs based on what you want rather than what you already know you can do. It’s in some sense the dual method to what people usually do.

  1. Silvia Adduci
    April 22, 2013 at 8:02 am

    Great advice, thank you MathBabe! Btw, what book on Bayesian Statistics do you recommend?


  2. John B
    April 23, 2013 at 11:08 am

    Interesting that you want to subject people to LinkedIn’s privacy policy, perhaps better called a ‘how we monetize your naïveté’ policy.


  3. sch
    April 24, 2013 at 12:41 am

    Thank you for this post. This is exactly something I needed at this point in time. I myself am stuck in a rut, in an unhappy postdoc after a PhD that did not quite turn out well.
    I have a mechanical engg. background, but I am trying to self learn machine learning. I am following the Caltech online course, and also considering following a couple of classes from the MIT OCW.
    But one question I had in particular for you, is that how much weight is given to self-learning/online course learning in a resume, as opposed to a proper university degree. Will employers look at such mentions in the resume seriously? Almost always I get discarded at the HR level itself it seems.


  4. April 24, 2013 at 1:03 am

    All organizations need the people who have the proper skills for the job. So increase the skills and get better job


  5. April 24, 2013 at 4:03 am

    @mathbabe Great article. It is also worth calling up recruiters to find out what skills are in demand.

    @sch As someone who is hiring a data scientist to work with digital data, practical knowledge and attitude trumps formal education in many business settings.
    Unless you’re going into R&D where you need an academic background in machine learning, you can get by with teaching yourself machine learning. What your employer probably wants to see is that you can apply your technical skills to real world problems. One way to do this would be: after you complete your Coursera course, collect some twitter data over a month and apply what you learned to make a prediction (that comes true), publish on your website (really show off your communication skills here) and share widely. This demonstrates initiative, curiosity and the ability to autonomously find interesting things.


  6. Sarub
    September 28, 2013 at 11:52 am

    “sometimes the “job requirements” data on LInkedIn dirty, but it’s still useful”


  1. September 28, 2013 at 8:12 am
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