
Interview with Nick Chamandy, statistician at Google - rayvega
http://simplystatistics.org/2013/02/15/interview-with-nick-chamandy-statistician-at-google/
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mamp
Very interesting interview, but I couldn't help be saddened by this:

"...my PhD research was on Gaussian random fields, with particular application
to brain imaging data. The bulk of my work at Google is in other areas, since
I work for the Ads Quality Team..."

I wonder how much innovation in health and other important areas are hampered
by the draw of bright people like Nick to the quest for ads and likes. Follow
the money I guess.

~~~
drunkpotato
This probably won't make you any less sad. Apologies in advance.

It's not simply a matter of throwing money and brains at healthcare. I used to
work on data analysis at a healthcare startup. There is no shortage of driven,
talented, intelligent people making businesses in that space. There are giant
firehoses of money in healthcare as well, both effective uses and of the
boondoggle variety.

So it's not solely the lure of money that draws bright people out of
healthcare. It could also be the realization that nothing you ever did would
be more effective than getting doctors to wash their hands, and doctors
_still_ don't do that.

Many people are brilliant enough to make wonderful tools, but it seems that
nobody is brilliant enough to overcome a system that doesn't want to use them.
Whereas if you make a tool that helps businesses sell advertisements, or
whatever the source of revenue, there is a higher chance people will actually
use it.

~~~
chime
> It could also be the realization that nothing you ever did would be more
> effective than getting doctors to wash their hands, and doctors still don't
> do that.

If you could invent some sort of mechanism that told doctors to wash hands, I
think they would be more likely to. Here's a few ways to do that:

1\. Google Glasses with OpenCV - touching blood, bodily fluids etc. sets an
alarm that doctors must either snooze till later or walk up to a sink to
silence.

2\. Special gloves that instantly turn green when coming in contact with the
most common bacteria.

3\. Wrist watch / mobile device that beeps loudly every time a doctor goes
from one patient to the next without stopping by a sink.

I agree that hospitals and doctors don't want to change because that takes
time. However, there is no point in giving up today because your work isn't
going to be popular for two more decades.

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goodwinb
I agree that knowing how to pull your own data is important. While you are
doing the tedious work of pulling data your brain will often spot a good idea
for a new experiment or see a new relationship in the data structure.

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DenisM
This reads less like an interview, and more like a carefully edited set of
answers to carefully chosen questions. Nothing wrong with that, I just was
surprised to see such well-connected piece to come out of a normally chaotic
process.

~~~
bearmf
Well, they definitely had to have it checked by someone senior at Google.
Usually they are very careful about disclosing what they do and employees have
strict NDAs.

------
east2west
A few things in this pierce struck a cord with me. I am a computer science
Ph.D. doing a postdoc in statistics department, working on biostatistics, and
as I am recently looking for a job and did a few interviews, some observations
may be helpful to job candidates.

First, people are pigeonholed into certain roles. Statisticians analyze data
and design experiments. They are expected to know classical statistical
fields, theorems, and their standard usage and caveats. Programmers should be
able to answer standard programming questions, such as dynamic programming,
balanced tree, etc., and of course write programs. In large company where
specialization is the mantra, expertise in both fields is not an assert in
interviews.

Second, multidisciplinary experience can be a liability. Since my experience
in statistics is nonstandard, more of signal processing than classical
statistical inference, I am not as conversant in classical statistical theory
as a statistical Ph.D. does, especially because genomic research often prefers
most basic methods, as in industry. Interviewers rightly ask about things they
know, and are not impressed if one cannot answer questions they learned in
graduate class. It is similar with computer engineering interviewers. They
will ask me to implement an interval tree or dynamic programming algorithm in
30 minutes. Most of my heavy programming is in numerical analysis and
optimization, where dynamic programming is very different from what a
programmer thinks it is.

Third, depth is not required in industry, and certainly not in interview.
Interviews now-days feel very much like college entrance exam in China or one
of those East Asian countries, where people are expected to regurgitate set
answers and the most important trick is to meet expectation. It is not
important to master materials but to have right answers. And the right answer
depends on the person asking question. One may considers SVM to be mainly a
kernel trick that molds nonlinear relation into linear function, while another
considers SVM as finite approximation of dynamic optimization with a
breakthrough in quadratic programming that efficiently solves the two-points
boundary problem coming out of dynamic optimization. This gets back to
pigeonholing roles. A professional statistician will prefer one while a
control/dynamic system expert will like the other. The killer is that some
interviewers ask questions with their preferred answer in mind, and the
questions can baffling to people with different background.

Forth, different companies demand different capabilities for supposedly same
roles. Data scientists can be as mundane as denormalization or as
sophisticated as inventing a way of causal inference. It is not always easy to
tell from ads. It is even less so when the company wants jack of all trades
and experts in all possible tasks. Ask very pointed questions.

Fifth, there is no advantage in being both good programmers and good
statisticians, at least in interviews. I have already noted several
disadvantages. People much prefer build inter-disciplinary teams each member
of which is tasked with one special area and let them talk. It works well. I
cannot think of anything that requires expertise in both programming and
statistics in one head. It may be a little slower, but not noticeable.

I am having doubts about a position in a large company because of too much
specialization. I like to derive an algorithm and implement it efficiently.
Even if I could get a position, and I could if I cram for interviews, I would
be at a disadvantage when others can concentrate on one area. Sadly, academia
is becoming very much like industry, except they count papers or grants
instead of make money. I am still looking for my niche.

I guess the gist of my rant is that today's job market demands specialization
and people better conform.

~~~
bearmf
Many of your points are spot on in that many of us had similar experiences.
However, I would disagree that being able to program can be a disadvantage in
any of good jobs you might be looking for. There is necessary communication
between members of the team and there are times when writing/reading code
results in much clearer and faster communication, even if you are discussing
some statistical approach.

In general you are right about pigeonholing/specialization in large companies.
But it usually happens after you are hired, not before. Right now, without
industry experience your skillset is very broad and it is hard to pigeonhole
you.

As for the interviews, if they are like an exam where you know questions in
advance you certainly should prepare. Sadly, this is usually not the case. As
you mentioned in your point (4), sometimes you have no idea what the role is
about. And there is no way to know because your recruiter doesn't want to get
specific or maybe has no idea herself.

Which brings me to an important point which you might be missing. If you are
applying for jobs through websites or by any other well-publicized way, you
will inevitably subjected to a vetting process. The interviewers do not know
anything about you and are basically looking for reasons not to hire you. If
you are very good technically, it increases your chances but it is no
guarantee of passing. Good communication skills are just as valuable.

It is different when you know someone inside the company, say your friend
recommends you. You will have more meaningful interviews from the start. Most
startup/small company hiring is done this way.

That said, with your background you should have little problems finding a job,
if current Big Data hype is to believe. But of course it depends on your
school, where you are looking etc.

