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.

Lost in translation: Academic work beyond academia

Carrie_Diaz Eaton
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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QUBESHub.org

 

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….