Academia trained me for a BIG career

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by Peter D. Horn

I am honored to share some career advice with the young and mathematically-inclined. When I fit that description, I felt a lack of diversity in the opinions and advice I was hearing from my mentors. This wasn’t their fault, but mine. Classic case of selection bias, as I only sought advice from my professors.  My first recommendation is to connect with many math folks who have walked a variety of paths to get a sense of what is out there (reading the posts on this blog is a great first step!).

When I was finishing up my math major, I felt there was more math for me to learn, and I went on to get a PhD in low-dimensional topology. As a grad student, I was encouraged to pursue a postdoc. By the time I was deep into my postdoc, I had a tenure-track job in my sights. It wasn’t until my third year into a tenure-track position that I evaluated my career choice and realized I would be happier doing something else.

I reached out to a few friends from grad school who went into government and industry, as well as a couple former academics who transferred to tech and finance jobs.  I did a little research to see what was out there, and found “data science” to be a broad enough field to entertain my intellectual curiosities (e.g. machine learning algorithms) while providing plenty of job security (i.e. strong business demand).  Currently, I am a data scientist at the MITRE Corporation, a non-profit company that does R&D for many federal agencies.  I love working at MITRE because I get to define what type of data scientist I want to be.  In my first year, I worked on research projects involving machine learning and agent-based models to drive policy analysis, and I prototyped a web-based simulation tool to explore workforce strategies for the VA.  It’s great to be at a company where the work is challenging and impactful.

While in the transition to industry, I realized that much of my academic training and some of my hobbies positioned me to be an attractive candidate.  As a math major/PhD candidate/professor, I had accrued a ton of experience teaching myself complex, abstract concepts. Employers seek out job candidates who can demonstrate the ability to pick up new things quickly.  Working in help centers/recitations/lectures, I had accrued a ton of experience explaining deep, technical material to non-technical audiences.  Employers like to hire teachers because they can put you in front of customers or use you to mentor young staff.  As a mathematician, you have surely gained similar experience.  Find a way to brag about your superpowers!

You’re going to need programming skills.  In my journey, I was lucky to have learned to code.  In college, I learned a bit of Java in CS 101.  In grad school, the math department hired me by the hour to maintain their website.  I chose to write up my homework in LaTeX.  Frequently, I would need to do some computations in Mathematica, Maple, Matlab, or Sage.  As a postdoc, I got bored one summer and wrote a couple of card games in Objective-C.  For a research paper, I needed to diagonalize some matrices over a non-commutative base ring, and I wrote the code to do this from scratch in Python.  Before I had even heard of data science, I had ten programming/markup languages under my belt, and I put all of them on my resumé to show employers that I am comfortable writing code.  If you don’t have experience programming, I recommend you pick up Python. It’s a good general purpose language.  Pick a project and use Python to attack it (e.g. implement matrix multiplication from scratch).

The last piece of advice I have is to acquire domain knowledge and to network. The biggest hurdle I had in my journey was learning to communicate with potential employers.  I decided to take online courses in data analytics and machine learning, and these courses taught me what people in industry care about, how they talk, and what tools they use.  I also participated in some coding and data science competitions online.  Since I had a noticable lack of business experience, these competitions were something I could point to as proof that I could do data science.  I would also recommend attending meetups in your area. In my experience, meetup people are very friendly and helpful.

Transitioning out of academia was scary, but it has been one of my best decisions.  At first I was worried I wouldn’t be what employers were looking for, but I learned that many employers want to build companies with people from diverse backgrounds. Don’t worry about trying to fit the mold.  Reach out to friends, former classmates, and friends of friends, and you will find all the support you need.

Blogpost: What are the obstacles to Math students entering BIG careers?

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by Dr. William D. Stone, Dean of Arts & Sciences and Professor of Mathematics, New Mexico Tech Mathematics Department

A strong Mathematical background is an excellent preparation for many exciting careers in business, industry, and government. So why don’t more of our students think in terms of these careers? I see two reasons.

The first reason I see, is that many faculty feel uncomfortable advising students into these paths, since they don’t have much experience with industry. Most of us went from college, to graduate school, to a faculty position. We don’t know that much about what a Mathematician does in a BIG career.

This is not an insurmountable problem. Do you have former students who have gone to industry jobs? Invite them back to talk to your Math Club. Or contact a BIG-SIGMAA member in your section and invite them to talk about what they do. Some of your students might get excited. Some students who may not have considered a math major, since they didn’t see career paths other than teaching, may now think about joining your department.

Another obstacle can be faculty attitude. If we think of it as a failure when one of our graduate students goes into a non-academic career, that attitude is conveyed even if we don’t say it directly.

To me, this attitude is short-sighted. Many students want to work on real, applied problems. We should be welcoming them into Mathematics, and helping them on their path. The more that scientists and engineers see the value of a mathematician on their research teams, the better for our profession. When we have students out in industry, we may find ourselves being drawn into some very interesting problems, with genuine consequences. It’s a win-win all around!

5 Things I Learned About Working at a National Lab

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After completing his Ph.D. in mathematics from the University of Florida, William Severa moved to the southwest to join Sandia National Laboratories as a full time researcher in the data-driven and neural computing department.

Pictured above: Sandia’s Z machine is the world’s most powerful and efficient laboratory radiation source.

1. It isn’t all cloak and dagger.

Yes, Sandia National Laboratories certainly works with sensitive and classified information—though what I learned is there’s a sizeable chunk of national labs’ work that is entirely unclassified and in-the-open. As a researcher, I continue to publish my work, and I can still discuss my research at conferences or meetings. More than that, there’s plenty of internal support to determine just what is sensitive and what isn’t, so you always know what you can or cannot say.

2. I still get to research cool ideas.

One of my worries about leaving academics was potentially losing research freedom. However, it turns out I’m afforded quite a bit of flexibility here. We are encouraged to pursue grants from a number of external funding agencies, and the Department of Energy has its own congressionally-authorized internal research funding called Lab Directed Research and Development (LDRD). These projects range in duration and scale, and the process provides a great mechanism to propose my own research ideas. LDRD projects are focused on high-risk, high-reward research, so they’re always up for the next great idea.

3. It’s an engaging interdisciplinary effort.

Every day I go to work with an incredibly diverse team. Since departments are centered on topics rather than degrees, we have a truly interdisciplinary effort. My co-workers’ backgrounds range from psychology and neuroscience to climate engineering and computer science (and mathematics!). Together we each use our expertise to contribute to a unified solution.

4. ‘Go ahead; Stretch out and try new things.’

I’m the type of person who is always excited to learn new things or apply what I know to new problems. However, as a pure mathematician leaving graduate school, I found it difficult to expand from my core expertise. At a national lab, I am constantly encouraged to approach new challenges. Some are close to my expertise, and some are a little farther. Either way this freedom lets me push my work into different and exciting directions.

5. They give us our breathing room.

Project timelines are on the order of years, not months. As such, we have the time to do basic research, not just push out a product. The exact schedule is, of course, dependent on the program. In my experience, the schedules have always been accommodating.

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Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Lost in translation: Academic work beyond academia

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In the airport again, in an #awkwardboardingselfie for #jmm2017

 

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Listening to interdisciplinary conversations as part of IUSE grant SUMMIT-P
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Dr. Carrie Diaz Eaton, Unity College

I have a pretty unusual set of grants. The skill set for my grants is the same: working with a variety of people from a variety of different backgrounds and disciplines to advance quantitative skills. For one of these grants, QUBES (Quantitative Undergraduate Biology Education and Synthesis, qubeshub.org), I am the QUBES Consortium liaison. My job is to reach out to all sorts of partner organizations, institutions, professional societies and faculty members interested in improving the quantitative skills of all students in life science. This means that I help people make connections across disciplinary silos, travel to conferences, hold leaderships positions in interdisciplinary undergraduate mathematics education, help write collaborative grants, manage budgets, manage communications, and assist in forming strategic partnership agreements. It turns out that my dissertation research in systems theory paid off quite well, since it turns out that social change theory and systems theory are more related than one would think.

That seems like a pretty academic outreach job description, right? But you can get a lot of the same skills through leadership positions at your own university. This isn’t my first experience working across disciplines. I was a President of the Spanish Language Club in college, on the executive board of my Service Sorority, had interdisciplinary course training in biology (including ecology, wildlife, and marine science) and mathematics (including computing and statistics). In grad school, I participated in interdisciplinary university-wide teaching training and book discussions. As faculty at a small liberal arts school, I formed a college-wide teaching discussion group, advised and employed students from a variety of majors, and collaborated with faculty in different departments to improve writing and applications in my math courses. I have also served on several college-wide committees including the general education committee and an accreditation committee, which also has forced me to collaborate regularly with a diverse set of stakeholders.

So how do these academic skills translate beyond academia? Here are some keywords:

  • Non-profit development and partnerships,
  • Working with a diverse set of colleagues across the world,
  • Grant and report writing,
  • Statistics and big data trends,
  • Careers in environmental biology,
  • Mathematical modeling education, undergraduate biology education research (and pretty much everything about the guiding document in biology, Vision and Change),
  • Systems thinking for social movement, systems change theory,
  • Project evaluation,
  • Grant and project management, organizational planning and workflow, team leadership,
  • Social media marketing,
  • 101 tips for travel to anywhere from Bangor, Maine (okay, maybe this is less relevant for most jobs, but I’m a fountain of information about direct flight options from the airports in my state),
  • and more…. *Whew*.

Best learning on the job ever, but on the other hand when people wonder what I do on grant time….