musings on music and life

May 22, 2017

#chemjobs realtalk

Filed under: Chemistry, Chemistry Jobs, Uncomfortable truths — sankirnam @ 10:48 am

Courtesy of u/OldLabRat on Reddit:

“[…] suppose you take your fresh PhD in chemistry/bio/whatever to Cambridge or the Research Triangle or some other center of industry and start knocking on doors. “Do you need any chemistry done?” you may ask “maybe somebody’s out sick? I’ll totally help out cheap, just throw me some lunch money.” They might be tempted, until they ask your qualifications. “We don’t have any PhD level openings”, they will sneer. And I think every Chemistry department has the legend of the guy who left his PhD off his resume, got a bottle-washing job at Big Industry Company, and then was a preferred internal candidate for their next PhD opening so he got it – the corollary of that legend is that the position really had been wired for someone else, and Big Industry Company made a new rule that anyone who was found to have a concealed PhD would be fired. So that sort of pavement-pounding approach won’t work, they’ve seen it already and enacted countermeasures – such is the meaning of this popular tale. Chemistry is a field which has deliberately put up barriers, has institutionalized methods to avoid hiring qualified applicants who really want the job and would do it very well, in favor of seeking members of an elite ‘network’ who possess that elusive quality of ‘fit’.

It’s often posted here that the key to chemistry employment is networking – which seems to mean being popular and charismatic. This really is a sign that becoming a chemist is more like becoming a fine artist, or a philosopher of postmodernism, or a rock star, than it is like becoming a schoolteacher or a car mechanic or a pastry chef. You do not simply offer enthusiasm and hard work, let alone skill, it’s about projecting an image of your awesomeness.

If people really need work done and want to hire somebody to do it, they don’t mess around in quite the same way. I don’t know of any schoolteachers who got their job by following the ‘networking’ methodology. Nobody runs up their credit cards attending teacher networking meetings and conference, where they listen eagerly to presentations from already-employed teachers before politely introducing themselves and passing out their aspiring-teacher business cards, afterwards going to the bar and buying drinks for successful already-employed teachers while asking them to share their wisdom and experiences and oh by the way here’s my card. Teachers don’t have time to sit at a bar and have drinks bought for them by aspiring applicants. They’ve got assessments to grade, activities to develop, chemicals to buy, lesson plans to write, professional development to attend to: work, in other words!

So I’d say chemistry is a ‘luxury’ profession right now, or at least society is treating it like one. Becoming a chemist is less like becoming a master electrician and more like becoming an opera singer.

Of course we’re more dependent upon the products of the chemical industry than ever. But honestly it doesn’t take a chemist to follow a procedure. It takes a chemist to write one, but after that it doesn’t. And even if you did want a chemist, there are plenty in China and India who will work for a slightly lower salary and are able to just dump their waste jugs down the sewer drain, which is ever so much more efficient and globally competitive!”

This. This is what I faced for two years while desperately trying to get a job in chemical research – it’s not enough to be competent, knowledgeable about the field and have domain expertise, but you also have to possess that elusive quality of “fit”, which could be anything, depending on the hiring manager’s mood that day. The “elite network” mentioned above is very real – it used to be solely an academic thing (i.e. 99% of new professors at most universities these days are from Harvard/Stanford/MIT/Caltech/Berkeley), but now, thanks the insane saturation in the chemistry job market at the PhD level, it has percolated into industry. The two biggest questions I would get while trying to convince people to at least give me some kind of opportunity at their companies would be:

  1. “If you’re as competent as you claim, why hasn’t someone hired you yet?”
  2. “If you’re as good as you claim to be, why isn’t your degree from Harvard/Stanford/MIT/Caltech/Berkeley?”

The tech industry, in contrast, is refreshingly egalitarian. It doesn’t have the saturation and craziness present in science hiring, and hiring decisions are not really swayed by academic pedigree or awesome networks but rather by a track record of tangible projects and results that you have brought to the table.

As I have said before, the first thing that needs to be done to fix this situation is to stop oversaturating the market with scientistsUniversities need to stop recruiting graduate students by the droves and invest more into ensuring the career success of existing students and postdocs. Of course, most professors will balk at this since their supply of dirt-cheap labor will be threatened – the incentive to change can only come from the top, from funding agencies such as the NSF and NIH.

May 7, 2017

Course Updates

Filed under: Coding, Data Science — sankirnam @ 2:22 pm

As they say, the path to self-improvement never ends…

I just finished the final exam for the course MITx: 6.00.2x Introduction to Computational Thinking and Data Science on EdX, and this motivated another summary post, similar to what I wrote last year on its prequel course, MITx : 6.00.1x. These two courses make up an introductory sequence to computer science, primarily geared at non-majors; there is a similar corresponding course taught on the MIT campus. While 6.00.1x is focused on getting students up to speed with Python and using it to write simple programs, 6.00.2x then looks at more fundamental CS concepts (e.g. greedy algorithms, search trees, etc.).

The presentation of the course is excellent – all the movies are in HD, and the text is clearly visible on the screen. Code snippets presented in the video lectures can also be downloaded later so that you can play with them. While Prof. Guttag’s lecturing style may not be quite as engaging as Prof. Malan’s (Harvard CS50), the MIT rigor is definitely there in every slide.

When it comes to the material and choice of topics in the course, the instructors decided to go for breadth rather than depth, and this led to a very rushed coverage of a lot of topics. At the same time, in an introductory course like this, you will have a lot of non-majors taking the course, and you want to give them a flavor of everything the subject has to offer. I have the same issues with the standard introductory general chemistry curriculum that is used today at most universities – in those, the topic coverage doesn’t necessarily translate to knowledge that may be very relevant even for future chemistry courses. In any case, after taking this course, I have the confidence to take future courses in computer science/programming, and am especially interested in trying out some basic algorithms courses. While I may not have the chops yet to crack open Knuth and study that on my own, I think a guided approach in another class would be valuable.

The problem sets, as always, were appropriately challenging. I made it through to the end of the course, which means that I probably fared better than other students who may have dropped out, but among those who stuck till the end, I think I am one of the weaker students. The course has a corresponding Slack channel, and most of the students who took the final said on the Slack channel that they were able to finish the final far faster than I did (of course, this may also be subject to reporting bias). The course lays an emphasis on OOP (Object-Oriented Programming), and so this teaches you how classes, objects, and their instances are implemented in Python.

I did try taking 6.00.2x last year immediately after completing 6.00.1x, but I got hopelessly stuck on the first problem set involving implementing a greedy algorithm. This time around, I powered through it, and was also able to finish the rest of the course. I’ve becoming pretty good at debugging my code using print() statements, and from what I hear, this is an extremely important skill.

I also took the course HarvardX: PH526x Using Python for Research (Edx) last year, and I figured that I would put my thoughts on that course in this post as well. This is a basic-to-intermediate level course that introduces the various Python libraries that are useful in scientific computing. Some of the elements of the Numpy stack are included (Numpy, pandas, matplotlib), as well as some other packages (Bokeh, cartopy, and others). As with any course, there is no way you can cover everything there is in any one of these packages, and so there is always a tradeoff for breadth vs. depth. 

All the coding assignments and homework problems for this course were done through DataCamp, which has its own quirks. I remember having issues getting a question involving PCA correct due to rounding errors (caused by implementing pca.fit_transform() vs. a sequential pca.fit() followed by a pca.transform()) which were not being accounted for by the grader.

PH526x also covers some vanilla python topics, including an introduction to list comprehensions, which is one of my favorite aspects of Python; once you understand the simple concept (e.g. initialize an empty list, iterate through something, and append to the list), you’ll begin to want to use it everywhere, and there’s nothing quite as satisfying as being able to write list comprehensions that compress several lines of code into one line. Prof. Onnela (the instructor) also covers the itertools module briefly, which is handy for generating things like “power sets”, which are used for coming up with brute-force algorithmic solutions.

In retrospect, I wish I had taken both of these courses before the “Data Science” bootcamp last year, but what’s done is done – actually, I wouldn’t have been able to, since PH526x was only released for the first time last November.

Another thing I’m curious about is the attrition rate for these courses; how many people actually finish? Knowing this might help to give me a better idea about whether I actually accomplished something significant or not.

As always, for those who are curious, I’m uploading everything to my github.

May 1, 2017

Mark your calendars!

Filed under: Carnatic Music — sankirnam @ 2:13 pm

Sowmya_SD_flyer_May_2017

I’m super excited for this program – it’s been my dream to play for Smt. Sowmya, especially since I’ve been listening to her concerts regularly since the 90’s. I have heard her concerts with my guru while sitting stage-side at the Madras Music Academy, no less.

Smt. Sowmya is one of my favorite artists; she presents a rare intellectual approach to music that is also purely classical. This is something that is sorely lacking in a lot of concerts by other artists nowadays, which are very flashy with a lot of “gimmicks” and little real substance.

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