Health tips for research groups

Nature asked scientists to recommend one thing that institutional and laboratory leaders could do to make science more productive, rigorous and happy.

And it might be someone’s full time job.

Read about it here!d41586-018-05146-5_15764614.jpg

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Moving into industry: You have most of the skills you need, and you can learn the few you are missing

Continuing on in my series about academia to industry, here’s how I gained the skills I needed to get a job outside of academia. 

I mentioned last week that I decided I wanted to leave academia, but then spent a full year preparing and learning what that meant. I started with an understanding that I had lots of skills, but wasn’t sure how those would translate to anything besides academia, and I ended with a job in industry. So I’ll start with the skills you do have that are applicable (yes, even you) and then talk about which ones you should make sure you have before starting to apply:

Skills you have:

Teaching and Presenting: One of the most critical traits desired in people who work with data is that they are able to communicate that data to other people. It is RARE in industry to have someone who can do the analyses AND communicate those to shareholders and relevant decision makers without them scratching their heads in confusion. But the good news: your entire academic career has prepared you for just this event. You know all those times you taught, and tried to get undergraduates to understand what you were talking about? Or those conferences where you build presentations to best present your results? They are training for doing that very thing which is VERY valuable in the world outside of academia. You already have this skill (high fives all around!).

Statistical knowledge: I know I sound like a jerk when I say this, but since moving into industry, I keep having to revise the list of things that I though everyone knew. I was asked “what is a p-value?” my first week, and have been asked more basic questions about statistics than I was ever asked in academia, even teaching undergraduates. You have a career full of experience doing experiments, collecting data, and making sense of that data by running statistical analyses. You are already ahead of the game in this regard.

Working hard: My mother told me: “The beginning of every job is the same, nose to the grindstone and work hard” and she was right. You need to be able to work harder and longer hours than you’re used to. Think back to your PhD – hours like that, only longer and in one place. The good news is that most of the multi-tasking you used to need to do is off your plate for now. The bad news is that means you have 9 + hours a day to focus on one task. Luckily, you’ve been doing this for awhile, and being a workaholic comes naturally to you. And even more luckily, this is not a long term commitment. You need to work hard at the beginning, but I’ve found it rare for people in my company (and other companies my friends have moved into) to work outside of regular business hours. This is a sprint, not a marathon.

Teaching yourself: In industry, they don’t expect you to be ready to go out of the box, like they often do in academia. In most industry environments, there is this whole process called “onboarding,” which I had never heard of before I started my current job. They know it’ll take you a little while to get up to speed. But, as an academic you’ve got a lifetime of experience teaching yourself. Great, it’ll serve you well. You can spend those first few months learning the things you’re going to need to know on the job, and that ability puts you ahead of your peers.

As you can see above, we’re already qualified, and have the potential to be successful. But you do need a few skills, at least for data science, that it is unlikely you have already attained. However, you can pick them up. Here are a few:

Python: Most academics I know code in R. The fancy ones also code in another language (I wrote simulations in C++)(I am not fancy though). While R is an excellent language for data analytics, and arguably the best in the world for statistics (come at me), for getting code into production, you need to know python. It’s similar to R, so shouldn’t be too hard, and if you have experience using R as a object oriented programming language, then it’ll be easier still. I learned python using DataCamp and strongly recommend it as a great resource. It has a jupyter notebook embedded in the site so you’re able to run code as you learn (learning by doing is important for me). But there are also books, youtube videos, coursera and udacity, and probably flash cards that will also help you learn this skill.

Querying database: In academia, we work on what are called “flat” data files, like CSV or excel files. Once you are looking at customer data, a flat file is simply too small. It’s like trying to open a NGS sequencing file – it’s just laughably impossible to do with your computer. So you need to learn how to query a database with schemas, like Postgres or MySQL. It’s a pretty simple programming language, and you’re going to learn to love to join tables. But this is a skill of taking a mountain of data, and finding the flower you want to study amidst the rocks. It takes time and practice, but is learnable.

Docker: I know I’m focusing a lot on the technology, but it has stood out to me as the one thing that’s very different between industry and academia. Docker is a way to run code locally that is entirely repeatable elsewhere. Your code is run within a “container” that is created with all the things you need installed in it, in a requirements file and an image. If you’re missing a requirement that exists locally on your computer, but is not in your file, the requirement won’t run. As a result, with the code base alone, you’re able to entirely reproduce your work, regardless of where you’re running your code from. When putting your work into deployment, this is especially awesome because you know that your container is ready to go before you deploy it. And it’s becoming ubiquitous across industry, so jump on that container bandwagon and download docker.

I’m sure I’m missing some things, and there are wealth of cultural differences that I haven’t even approached (feel free to comment below). But the gist of this post is that you ALREADY HAVE MOST OF THE SKILLS YOU NEED TO SUCCEED IN INDUSTRY. And the ones you lack are trival to learn. Now that we know this,next week we’ll talk  about one of the things you need to do to actually apply: a linkedIn profile.

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You’ve presented your research, you’ve taught. You know how to communicate results, you’re already ahead of the game.

The Worst Stock Photos of Scientists

I used to be asked often “what is it you do?”. And it’s hard to explain.

I do research, I ask questions, I answer them to the best of my ability.

However, I do not do so in my lingerie, or while staring at small pieces of dry ice.

Which is what makes the hashtag  on twitter so hilarious. See a few below, or a larger collection here.

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Coming out as a non-academic

I recently made a massive transition. I left my postdoc at Martin-Luther University, and started a job as a data scientist for a fintech. Full on transition from academia to industry.

And telling my academic colleagues was, and still is hard.

Which is why I found this article about the similarities between coming out as a proud gay woman and coming out as a non-academic so interesting.

And great insight for those of us who might also be thinking of making the transition.

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Scientists at Work

This year’s Nature #ScientistAtWork photo contest winners and runners up are revealed and they are awesome.

Here are a few, but the whole collection can be found here.

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Field Work! I have actually been in this same spot, but with a mini van in the Hebrides Islands.

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At the March for Science, because science should be diverse.

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Volcanic Salt Plains in Ethiopia.

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Lowering a boat to abseil a boat into a 40-metre sinkhole in Arnhem Land to investigate the area’s geological record.

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Space, from antarctica

Alternatively academic

Danielle is another excellent scientist that I’m happy to count as a friend. She’s smart, funny, interesting, and gives excellent advice on a wide variety of topics for which she is considered an expert. These topics include (but are not limited to): roller derby officiating, traditional cocktails, bird pheromones, and being a science boss lady. While her role is still very much an academic position, it is not a traditional position. As a result, she was happy to share her thoughts on “alternate academia”. 

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I get a lot of questions about my job, because although I am an established academic at a university, I am not a professor. My official title is Managing Director of the BEACON Center for the Study of Evolution in Action, headquartered at Michigan State University. My position is a blend of administration and research.

I am responsible for the operations of a multi-institution, multi-disciplinary NSF Science and Technology Center. We have over 600 members at 5 universities, and it is my job to ensure that all members have access to the resources they need, like cross-disciplinary training, seminars, funding opportunities, collaborators, and our annual conference. I am our primary liaison with our funder, the National Science Foundation. One of my primary responsibilities in that capacity is compiling and submitting our Annual Report, which typically runs well over 200 pages long and documents dozens of research and educational efforts, as well as our collaborations with industrial affiliates and efforts to increase diversity in STEM. I coordinate and run our annual NSF Site Visit, in which we spend a (very) full day presenting our research, education, diversity, and knowledge transfer efforts to a panel of external reviewers, who determine whether we are meeting our goals and decide whether to recommend that our funding be continued for the following year. I organize our annual BEACON Congress, a 3-day conference for our members and other interested visitors. This conference features concurrent tracks with contributed talks, member-organized symposia, workshops, and brainstorming “sandbox” sessions where people can discuss new ideas and collaborations.

These administrative activities account for about 70% of my work efforts. Most of my remaining time is spent on research. I maintain an active research program in evolutionary biology and animal behavior. I study chemical communication in songbirds, which involves both field and lab work and collaborations with chemists, microbiologists, and other evolutionary biologists. I supervise a postdoctoral researcher, and I have also served as the external member of two doctoral dissertation committees. Finally, I also do a fairly significant amount of service to the field, reviewing journal manuscripts and grant proposals and serving on NSF review panels.

How did I get here? Well, to be honest, I was initially interested in a more typical tenure-track career. I applied to well over 100 tenure-track positions over a couple of years, was invited to a handful of campus interviews, and received one job offer that did not suit my needs. After the last round of interviews, I had begun to sense that the realities of a tenure-track position did not match the career I had envisioned, and started to consider alternative paths. I was a postdoc at Indiana University at the time, and started looking around at the other researchers I admired there. I realized there were quite a few people involved in running research centers who appeared to have the perfect job, in my opinion anyway. I started thinking about looking for these kinds of opportunities, but I didn’t really know where to start.

Lucky for me, just a few weeks later, a job ad was posted on the Evol Dir listserve that seemed to be exactly what I was looking for – a brand new NSF-funded center was hiring a Managing Director. They wanted a person who was an active researcher in evolutionary biology, not a pure administrator, so that the person in this position could understand and communicate the science done at this center. I had no idea whether they would consider me even remotely qualified, but I worked harder on that job application than I ever had on any tenure-track application. I was invited to interview, and shortly afterwards they offered me the job.

It’s difficult to give advice to someone who is interested in a similar career, because there is no defined path, and there is no central resource to find jobs like mine. Often, these “alt-ac” jobs are what you make of them. I tell people to keep your eyes open and network as much as you can. If you are looking to make a career change, make sure people know about it. Graduate students are often afraid to admit that they don’t want a tenure-track job, for fear of “disappointing” their advisor. In my experience, most advisors just want their students to be successful, on whatever path they follow! Jobs like this are often not advertised as openly as mine was. If people know that you are looking for an opportunity, they will mention your name when they hear about such things. Things don’t always work out the way you think they will, and that can be for the best.

The down side of my position is that it has an end date. NSF STC funding lasts for a maximum of 10 years. We are currently in the second half of year 8. Where do I go from here? It’s too soon to know for sure, but there are a number of possibilities that interest me. At the top of my list is working for the National Science Foundation. I’ve learned a lot about how NSF works in my time here, and also through experience serving on proposal review panels. I am particularly interested in the STC program itself, as their model of facilitating collaborative “team science” is inspiring – and it’s working!

More Advice for Graduate Students

The unfortunate consequence of having completed graduate school is that we think we know everything about getting through. Which is likely why I’ve read about a dozen “advice for graduate students” columns.

However, this one from Dorsa Amir really stands out among the pack.

Best piece of advice: Other people’s successes are not your failures.

But really they are all good. Read it here.

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