Tag: wave

I’m not a data scientist

After a little over four years of trying to ride a buzzword wave, I hereby formally cease to call myself a data scientist. There are some ongoing assignments where that term is used to refer to me, and that usage will continue, but going forward I’m not marketing myself as a “data scientist”, and will not use the phrase “data science” to describe my work.

The basic problem is that over time the term has come to mean something rather specific, and that doesn’t represent me and what I do at all. So why did I go through this long journey of calling myself a “data scientist”, trying to fit in in the “data science community” and now exiting?

It all started with a need to easily describe what I do.

To recall, my last proper full-time job was as a Quant at a leading investment bank, when I got this idea that rather than building obscure models for trading obscure corner cases, I might as well use use my model-building skills to solve “real problems” in other industries which were back then not as well served by quants.

So I started calling myself a “Quant consultant”, except that nobody really knew what “quant” meant. I got variously described as a “technologist” and a “statistician” and “data monkey” and what not, none of which really captured what I was actually doing – using data and building models to help companies improve their businesses.

And then “data science” happened. I forget where I first came across this term, but I had been primed for it by reading Hal Varian saying that the “sexiest job in the next ten years will be statisticians”. I must mention that I had never come across the original post by DJ Patil and Thomas Davenport (that introduces the term) until I looked for it for my newsletter last year.

All I saw was “data” and “science”. I used data in my work, and I tried to bring science into the way my clients thought. And by 2014, Data Science had started becoming a thing. And I decided to ride the wave.

Now, data science has always been what artificial intelligence pioneer Marvin Minsky called a “suitcase term” – words or phrases that mean different things to different people (I heard about the concept first from this brilliant article on the “seven deadly sins of AI predictions“).

For some people, as long as some data is involved, and you do something remotely scientific it is data science. For others, it is about the use of sophisticated methods on data in order to extract insights. Some others conflate data science with statistics. For some others, only “machine learning” (another suitcase term!) is data science. And in the job market, “data scientist” can sometimes be interpreted as “glorified Python programmer”.

And right from inception, there were the data science jokes, like this one:

It is pertinent to put a whole list of it here.

‘Data Scientist’ is a Data Analyst who lives in California”
“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”
“A data scientist is a business analyst who lives in New York.”
“A data scientist is a statistician who lives in San Francisco.”
“Data Science is statistics on a Mac.”

I loved these jokes, and thought I had found this term that had rather accurately described me. Except that it didn’t.

The thing with suitcase terms is that they evolve over time, as they start getting used differentially in different contexts. And so it was with data science. Over time, it has been used in a dominant fashion by people who mean it in the “machine learning” sense of the term. In fact, in most circles, the defining features of data scientists is the ability to write code in python, and to use the scikit learn package – neither of which is my distinguishing feature.

While this dissociation with the phrase “data science” has been coming for a long time (especially after my disastrous experience in the London job market in 2017), the final triggers I guess were a series of posts I wrote on LinkedIn in August/September this year.

The good thing about writing is that it helps you clarify your mind, and as I ranted about what I think data science should be, I realised over time that what I have in mind as “data science” is very different from what the broad market has in mind as “data science”. As per the market definition, just doing science with data isn’t data science any more – instead it is defined rather narrowly as a part of the software engineering stack where problems are solved based on building machine learning models that take data as input.

So it is prudent that I stop using the phrase “data science” and “data scientist” to describe myself and the work that I do.

PS: My newsletter will continue to be called “the art of data science”. The name gets “grandfathered” along with other ongoing assignments where I use the term “data science”.

Bayesian Recognition and the Inverse Charlie Chaplin Principle

So I bumped into Deepa at a coffee shop this evening. And she almost refused to recognise me. It turned out to be a case of Bayesian Recognition having gone wrong. And then followed in quick succession by a case of Inverse Charlie Chaplin Principle.

So I was sitting at this coffee shop in Jayanagar meeting an old acquaintance, and Deepa walked in, along with a couple of other people. It took me a while to recognise her, but presently I did, and it turned out that by then she was seated at a table such that we were directly facing each other, with some thirty feet between us (by now I was positive it was her).

I looked at her for a bit, waiting for her to recognise me. She didn’t. I got doubts on whether it was her, and almost took out my phone to message and ask her if it was indeed her. But then I decided it was a silly thing to do, and I should go for it the natural way. So I looked at her again, and looked at her for so long that if she were a stranger she would have thought I was leching at her (so you know that I was quite confident now that it was indeed Deepa). No response.

I started waving, with both arms. She was now looking at me, but past me. I continued waving, and I don’t know what my old acquaintance who I was talking to was thinking by now. And finally a wave back. And we got both got up, and walked towards each other, and started talking.

The Charlie Chaplin principle comes from this scene in a Charlie Chaplin movie which I can’t remember right now where he is standing in front of a statue of the king. Everyone who goes past him salutes him, and he feels high that everyone is saluting him, while everyone in effect is saluting the statue of the king behind him.

Thus, the “Charlie Chaplin Principle” refers to the case where you think someone is smiling at you or waving at you or saluting you, and it turns out that they are doing that to someone who is collinear with you and them. Thus, you are like Charlie Chaplin, stupidly feeling happy about this person smiling/waving/saluting at you while it is someone else that they are addressing.

Like all good principles, this one too has an inverse – which we shall call the “Inverse Charlie Chaplin Principle”. In this one, someone is smiling or waving or blowing kisses at you, and you assume that the gesture is intended to someone else who is collinear with the two of you. Thus, you take no notice of the smile or wave or blown kiss, and get on with life, with the likelihood that you are pissing off the person who is smiling or waving or blowing kisses at you!

Both these effects have happened to me a few times, and I’ve been on both sides of both effects. And an instance of the Inverse principle happened today.

Deepa claimed that she initially failed to recognise me because she assumed that I’m in Spain, and that thus there’s no chance I would be in Jayanagar this evening (clearly she reads this blog, but not so regularly!). Thus, she eliminated me from her search space and was unable to fit my face to anyone else she knows.

Then when I started waving, the Inverse Charlie Chaplin Principle took over. Bizarrely (there was no one between us in the cafe save the acquaintance I was talking to, and I wouldn’t be waving wildly at someone at the same table for two as me; and Deepa was sitting with her back to the wall of the cafe so I presumably could not have been waving at anyone else behind her), she assumed that I was waving to someone else (or so that was her claim), and that it took time for her to realise that it was her that I was actually waving to!

Considering how Bayesian Recognition can throw you off, I’m prepared to forgive her. But I didn’t imagine that Bayesian Recognition would throw her off so much that it would cause an Inverse Charlie Chaplin Effect on her!

Oh, I must mention that I have grown a stubble (the razor I took on my trip to Europe was no good), and that she mentioned about not wearing her glasses today. Whatever!