Opportunity Begets Opportunity

You hear “opportunity begets opportunity” like a broken record. It is trite, but very true. I’ve been known to say it with sometimes extreme frequency and I wholly defend it.

A brief example from my week: the Census Bureau launched a new quarterly event called Census Talks which is an hour-long TED-esque event. I saw the call for the event a few weeks ago and instantly knew I’d send in an application to speak even though I’d only been at the Bureau for a month at that point. I figured it would be both fun and a good advertising opportunity.

I delivered the talk this past Thursday and got an incredibly positive reaction. It was essentially my journey from theoretical physicist to data scientist working in public service. A couple people expressed that they were blown away that I was willing, and able, to give such a solid talk after being at the Bureau for only a month and change, especially when the other two speakers were significantly more senior than me.

After the talk I was approached and emailed by multiple attendees who thanked me for the talk and wanted to tell me about the data science related things they were doing at Census. Now, only two days after the event, I have another invite to speak and will be working with someone to launch a data science journal club. These aren’t guaranteed to be life changing opportunities, but they too may lead into more opportunities such as even more speaking invitations or new and exciting projects at work.

Put yourself out there. Great things aren’t prone to come your way if you just stay in your lane.

The Little Things

Something I love about learning Python is the ability to rapidly answer your own questions particularly on basic functionality. I was writing a little script and I knew I wanted to use a dictionary for one aspect of it. However, I wasn't sure if I could use the append method on a particular key of the dict. I figured I could since the item was a string, but maybe the behavior is weird?

Fear not! I opened a console and did:

dict_test = {'testkey': [1,2]}

And as hoped it output [1,2,3]! This sort of feedback and ease of self-learning is amazing.

These sort of little things are a huge part of what makes learning to be a better programmer so much fun!

Grad School Plus

My career interests are not in academia, and yet I'm a postdoc at a university. This is certainly a plot twist in my life that I could not have predicted 3 years ago when I decided that academia was not the life for me.

Why am I here then? One of my mentors, Tepring Piquado, often referred to her experience as a postdoc as grad school plus. This always felt reasonable since postdocs are technically training positions but I don't feel it fully made sense until I was faced with doing a postdoc.

My postdoc is a bit of a field switch. My PhD was in developing theoretical understanding for thermal density functional theory using simple models and now I'm working in applying ground-state density functional theory to real (and comparatively complicated!) systems. This is a key aspect to grad school plus. I spent 5 years becoming an expert in one thing and now I'm expected to become competent and productive in something entirely new within a much shorter span of time. This is powerful preparation for the real-world and is a type of preparation that just doing grad school doesn't prepare you for.

During grad school it is easy to lose sight of the big picture especially in terms of what you know and don't know. I made it a point during my PhD to learn skills beyond the scope of my research, such as science communication, but as I neared the end I painfully noticed that I was lacking a skill expected of most theoretical physicists. My research group was old school in that we did lots of pencil and paper theory and simple calculations whereas most theorists spend a lot of time coding. So, grad school plus is a perfect time to fill the holes left by grad school -- in my case I've spent the past month learning Python and neural networks. By time my postdoc ends I intend to have a skill set that allows me to enter data science if I so choose.

The over riding theme: it is easy to burrow into a niche and a postdoc is a golden opportunity to expand your skills so that you're more competitive on the job market. A PhD is a powerful thing (or so they tell me) and having diverse skills to back it up is even better. I've always fancied myself more of a jack of all trades and I'm enjoying this opportunity to move back towards that role.

Raise your hand if you know of machine learning.

I sent out this tweet while attending a fantastic session at the AAAS Annual Meeting last week in Boston. This moment has been resonating with me and I want to signal boost it further.

The speaker asked the room to raise their hand if they thought 90% or more people in UK knew the term machine learning. A small, but non-trivial, number of hands went up. 70%? Even more. 40%? A vast majority of hands went up predicting that the vast majority of UK citizens knew the term. The speaker, if I recall, then jumped to if we thought only around 10% knew the term. My hand, and that of a few others, went up. The actual figure is 9%. That does not surprise me. What surprises me, and troubles me, is that most of the scientists in the room thought the number was much bigger.

That's a communication and understanding problem.

We talk about how scientists need to do a better job at communicating with lay-audiences. We need to learn how to distill our messages and tell our stories in engaging ways. I agree! But we need to know what our audiences know, and more importantly what they don't know. I have a strong feeling from talking to friends and colleagues that many scientists have a vast overestimation of what the general population knows. We're always told to know our audience when giving technical talks and this is even more true with lay-audiences. Science communication rock stars probably have a good sense of what is known, but does your average scientist?

Scientists need to not be out of touch.