Your resume is your cover photo

During your transition from academics to Data Analytics and Data Science you will need to craft a resume. This is a relatively daunting task! And how you present yourself really matters.

First off, what is the goal of your resume? The goal is to generate interest in you. That is to say, your resume is an advertisement. A cover photo of you for potential employers. Its job is to convey the right information in the right way to get you to the next step in the interview process. It’s unlikely that your resume will land you a job on its own. In order to get a job, you will eventually have to show up in person and present yourself at an interview (But that’s a topic for later!). All this to say, the presentation of the information is as important as the information itself. So much so, that I will just talk about presentation for this article.

There are many well-meaning, but unhelpful, resume reviewers in the world. These people may or may not be beneficial to your journey.  I have seen resumes crafted by masters students which look as though they were written by a high school student because a resume/career center coached the applicant into including no details, lots of space, and vacuous technical statements.  I have also seen resumes which are actually abbreviated CVs and are over crowded with unnecessary information.  So, be a little wary of your enthusiastic parents from other fields and friendly school career centers who are mostly focused on non-technical fields. They may guide you towards presentation which does not deliver your desired message. For reviewers, I strongly encourage finding someone within the field you are aiming for.

The next step is to look at as many resumes as you can get your hands on!  Ask your friends, older peers, and classmates. Glance around at job fairs and see what the other resumes look like. It’s important to know what recruiters are seeing so you can understand what the local landscape of resumes looks like for your area.

Presentation Matters! For my field, analytics and data science in retail, we care a great deal about communication skills. Our analysts and scientists are required to communicate with non-technical business partners on a daily basis. So, I often consider the design of someone’s resume as a direct reflection on their ability to present information in a compelling way. Can the applicant present the information she knows best (herself!) in a compelling way? Does she have a clear formatting method that highlights her data visualization or effective communication skills?

A notable presentation pitfall I experienced recently: Does your resume look identical to your colleague’s? I was once handed 4 resumes during a recruitment fair which looked almost identical. In fact, along with identical presentation, the content was similar as well. The candidates went to the same school and many worked at the same company! So, despite being well laid out and having good material, I had no good way to distinguish between candidates. It became and accidental game of “who wore it best?”. And I promise, that is not a game you want to be playing with your resume.

A second common pitfall is length.  If you have a PhD or 10+ years of experience after undergraduate, then I’m expecting a 2 page resume. It’s not mandatory, but if I don’t see all the information I need on the first page and there is no second page, then I’ll get nervous about your ability to determine what’s important to share. The best stuff should be on page 1, but if I like what I see there, then I’m hoping to turn the page over and learn more about your community engagement within your field, leadership skills and, perhaps, accolades/publications.

The third risk is to fall too far towards presentation. If your resume looks too far outside the norm for the field, then you may be met with unconscious bias from the hiring manager. That bias might be good or bad, so, just be aware of when you are doing it and know it’s a risk. I recommend trying to be a 1 or 2 standard deviations from the cookie-cutter resume, but not so many as 4+ std dev from average. Depending on the background of the hiring person and the company culture, your beautiful and artistic resume may be seen as not-technical or not-serious enough if your resume is too flashy. On the other hand, for a company that prides itself on style, if your resume is too plain and serious, then the hiring manager may wonder if you have the appropriate interest in fashion to do well as an analytical expert in that domain.

In summary, get resume tips from helpers in your field. Look at as many resumes as you can to spot patterns and see what trends are present.  Then, follow the mold, but be unique! Just like your cover photo: there are standard conventions and how you uphold or break those conventions says a lot about who you are.

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Leaving Academics

For academics, mid-winter is a reflective time of year. It was about this time, 3 years ago that I wrote an article reflecting on my own move to industry. Perhaps it’s because this is the time when academic jobs are choosing their applicants. So there is great anxiety wrapped up in “will I have a job come September?” Perhaps it’s because we are 1/2 way through the school year and those papers aren’t written and we have a new crop of confused and needy students.  Or perhaps it’s because it’s dark and cold and oppressive outside… and, ya’ll, it’s just SO snowy this year.

For me, this year in particular, I have many friends who are talking about leaving academics. Women mostly. Scratch that, they are women completely. I think this year is a confluence of political depression and the magic 5 year mark.  We’ve all been done with our PhDs for about 5 years. 10,000 hours we’ve spent in our respective careers; me in Industry, them in Academia. Though, to be fair, they have probably spent more hours on their careers than I have. And how far did it get each of us?

In this way, I get this very personal view into the leaky pipe problem. I see individuals making choices about their life and their adorable newborns and their priorities. I coach individuals as they make their transition into Industry. And I have come to a few conclusions.

For one, I think there is a messaging bias in Academics. While I was in academics, I was bombarded with the message that life outside academics was worthless. Simply bombarded. Not one of my professors enthusiastically agreed with my choice. But why would they? They chose to stay in academics. Almost by definition, they would not coach someone to leave academics. It’s also counter productive for a professor to coach their students to leave. The professor’s success is measured by the success of their students within academics.

Secondly, as one begins to leave the academic campus, it feels like she are falling off a cliff and she can never go back. Because, like, no one ever does! There are very few examples of individuals who went to Industry and then returned to a good position in Academics. Initially, I thought this was evidence that it wasn’t possible. Somehow being in Industry soils you to the purity of Academics. It’s a one-way trip! Be extra sure you want to make that choice because there is NO GOING BACK. But now, 5 years later, I have a different theory to explain why no one returns to the ivory tower. It’s because people do not want to!

Academics requires a brutal commitment level. It’s like being a professional athlete (the odds are about the same). Except, that you almost never get to win a game, but somehow you have to keep trying. It’s hard. And so, the decisions feels like a choice: “Quit” or “Don’t quit”?  I disagree with this framing. In Industry, we have these phrases for people who want to change jobs. You can either be running from something you hate in your current job or running towards something that you’d like in your new role. The Quit/Don’t Quit framing means you can only run from Academics, but you can never run towards Industry.  But what if Industry is actually a super lovely place to be?

Turns out, for me, Industry is a good place to be a balanced adult. I work during the day and spend time with my family at night and on weekends. I am happy. Even the most academically minded colleagues I have out here in Industry balance their desires by doing independent contracting for the government or some other more “academic” activity. I don’t know anyone who has retroactively wished they stayed in Academics. But if that’s you, then please let me know! I would love to learn why you feel this way.

Lastly, I have seen that the decision to shift from Academics to Industry is deeply personal but almost always influenced by a desire for improved mental health. Just recently, the BBS ran a tragic story about a professor who committed suicide because the load was too great. The first two elements: the messaging bias and the one-way trip combine to make it feel like there is no way out. No other options. I believe that the mental toll of being in academics cannot be understated. And finding a path towards a (hopefully) more balanced life, is always personal and unique.

Another, more positive, very personal change that influences people are babies. Babies! Nature just published a statistic that >50% of women leave STEM fields after their first child. Now this study wasn’t just about academics. But I think it raises an important point.  Mother’s brains physically change after birth. [Boston Globe, NYTimes] I literally think differently than I did pre-baby. I have observed a rapid adjustment in my priorities. I just don’t care as much about some things which used to be vital to me. I’m not a totally different person, but just epsilon different enough that I make some different choices. I can understand how someone who was very committed to their academic career could change course once they spend time with their newest family member. There is just some things, for me, which feel more important, more critical than throwing myself at a wall to maybe, possibly, grow the collective human knowledge by an infinitesimal amount.

So, in conclusion, if you are someone who is in Academics who is thinking about moving towards Industry, then take comfort. You are not alone and your feelings are valid. Have some tea and start thinking and learning. (I have some resources collected here that might help you.) Read about other’s journeys and the process of building a LinkedIn profile. You are exceptionally good at learning and understanding new ideas. I have every confidence that if you want to try Industry for a while, that there is a company out there that would love to hire you.


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Raising a Neural Net

I’ve been a little busy over the past year or so. You see, I have been creating the most advanced neural net known to mankind. It took me 9 months to code it and get it just right. My husband helped to provide some of the code, I provided the rest. As often happens, we repurposed some of the code from people we trust. It’s basically open source. Almost anyone can do it. Compiling it wasn’t the most fun I’ve ever had… There were some late nights, and I definitely lost some sleep. But my neural net turned out really cute! I lucked out. I didn’t plan for it, but it has dimples!

Now that she is here, I’m training my model. It will take a long time to train. 18 years by some accounts. Maybe more? I often wonder if the training time depends more on me training it well or its implicit structure?  It has many layers and a variety of activation functions. There is definitely drop out automatically encoded. By all accounts, it’s very sophisticated. The only  major downside is that I have to feed it all its labeled and unlabeled data manually.

When I put together my training data, I think a lot about ethics and values. I want my little neutral net to eventually make good decisions and be kind to other programs. It’s not so easy to build an unbiased model. Because I’m creating the training set, it’s up to me (and my family) to teach it well. Luckily, it’s not a one and done situation. If, in a few years, I learn that my neural net has learned to hit other neural nets, then I can ramp up the training data to try discourage that activity. But, I guess ultimately, it’s still a black box. I’ll never understand exactly why it made each decision!

Having a human neural net makes me think differently about how I might train my digital neural nets in the future… Meanwhile, my neural net woke up from her defragging and sleep processes, so I need to go. I get to go to stack more soft blocks so she can collect more unlabeled data about gravity!

Posted in Communicating Math, data science | Tagged , , , , , | 4 Comments