## Hiring a Data Scientist

Sun 21 February 2016
by

Steven
I recently found myself hiring for the position of data scientist. While I had interviewed
candidates at previous jobs, I am now in a considerably smaller group with a greater role
in the hiring process. Here are a few of my thoughts on the process:

### we are reading this.

We read all the resumes sent to us, and all the cover letters (of which there were not enough).
In fact, nearly all the resumes were read by two of us. Perhaps this is not the case at larger
firms who receive hundreds of resumes for a job (or is that a myth?), but we were eagerly
looking for the right candidate, which meant actively researching candidates. Unfortunately
some people treat job applications like lottery tickets: an attempt to net a low probability
large payoff with minimal investment. Like the lottery, you probably have to apply scattershot
to hundreds of jobs to win.

This kind of lottery-ticket application is easy to spot, as no perceptible effort has been
applied. A job application without a cover letter, even a few sentences, feels wrong. It's like
sitting at a bar and someone tries to pick you up by showing you their car keys and class ring
without talking to you. While the cover letter is nominally your chance to personalize your
application, it should be sincere, even at the cost of brevity. Continuing the analogy, it
shouldn't sound like a pickup line.

### blah blah Ginger blah blah blah Ginger

One of the candidates tailored their resume for us, emboldening those skills which we requested in
the job posting: **Python** blah blah blah, **MySQL** blah blah.
I felt a tiny bit manipulated when I realized they had done this, but it made
it *so* easy to see that they matched the minimum qualifications in …

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## It's Madness!

Sat 02 January 2016
by

Steven E. Pav
I recently released a package to CRAN called
madness. The eponymous object
supports 'multivariate' automatic
differentiation by forward accumulation. By 'multivariate', I mean it allows
you to track (and automatically computes) the derivative of a scalar, or
vector, or matrix, or multidimensional array with respect to a scalar, vector,
matrix or multidimensional array.

The primary use case in mind is the
multivariate delta method,
where one has an estimate of a population quantity and the variance-covariance
of the same, and wants to perform inference on some transform of that
population quantity. With the values stored in a `madness`

object, one merely
performs the transforms directly on the estimate, and the derivatives are
computed automatically. A secondary use case would be for the automatic
computation of gradients when optimizing some complex function, *e.g.* in the
computation of the MLE of some quantity.

A `madness`

object contains a value, `val`

, as well as the derivative of
`val`

with respect to some \(X\), called `dvdx`

. The derivative is stored
as a *matrix* in 'numerator layout' convention: if `val`

holds
\(m\) values, and \(X\) holds \(n\) values, then `dvdx`

is a \(m \times n\) matrix.
This unfortunately means that a gradient is stored as a *row* vector.
Numerator layout feels more natural (to me, at least) when propagating
derivatives via the chain rule.

For convenience, one can also store the 'tags' of the value and \(X\), in
`vtag`

and `xtag`

, respectively. The `vtag`

will be modified when computations
are performed, which can be useful for debugging. One can also store
the variance-covariance matrix of \(X\) in `varx`

.

Here is an example session showing the use of a `madness`

object. Note that by
default if one does not feed in `dvdx`

, the object constructor assumes that
the value *is equal to* \(X\), and so …

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## Inference on Sorts

Wed 30 December 2015
by

Steven E. Pav
Previously, I described a
model for taste preference appropriate
for some experiments in cocktail design I conducted years ago.
I noted that this model was so elegant and simple, it must have been discovered
previously, and have a rich theory around it. In the two weeks since then,
I discovered a new paper on arxiv about
inference on ranks from comparisons. They review a model much like the one I
outlined, calling it the
Bradley-Terry-Luce
model. (Hey, look, there is indeed a
package on CRAN
for this with a vignette!)

The paper by Shah and Wainright outlines a very simple method for estimating
the top \(k\) of \(n\) participants when the contests include exactly two
participants each. If I am reading it correctly, you take the average number
of observed wins for each contestant, then grab the top \(k\). They prove that
this algorithm is optimal under certain conditions. This seems to me
like an ideal outcome for a research result: the algorithm is dead simple,
and people have likely been using it for years, while the proof is somewhat
intricate. Unfortunately, it does not seem straightforward to generalize
the algorithm to the case where there are covariates, or 'features' about
the various contestants, nor necessarily to the case of multiple contestants
in a given contest. The Bradley-Terry model, on the other hand, is readily
adaptable to these modifications.

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## Using vim as an IDE

Tue 29 December 2015
by

Steven E. Pav
For a number of years now, I have been using vim as a lightweight IDE. The
ecosystem of vim addons is rich. There are numerous plugins for creating tags
to navigate a project, browse files in directories, highlight syntax and so on.
What really makes it an IDE is the ability to execute code within the context
of vim.
I realize this probably sounds 'charming' to disciples of that other
text editor, but it might seem like an unnatural urge to my vim
correligionists. The piece that glues it all together is `vim-conque`

. The
easiest way to get conque in ubuntu is via `apt`

as follows:

sudo apt-get install vim-addon-manager vim-conque
sudo vim-addons -w install conqueterm

The skinny on using conque is that you can visual-select code that you are
editing, hit `<F9>`

, and it will be transfered to the execution window,
newlines and all. So you can test out code while you are writing it. You
can also work the other way, testing out code in a REPL, then, when it is
working as expected, escape insert mode in the REPL, yank the working code to a
register, and copy it into the file you are working on.

## Dockerfile or it didn't happen!

This kind of advice is a bit abstract, so I put a working example on
github and
dockerhub. You can run it
yourself via docker:

# this might take a little while to download
docker pull shabbychef/vim-conque
docker run --rm -it shabbychef/vim-conque

This will feel a bit odd: when you run the last command, you are in vim, but
you are in vim in a docker container. When you terminate, your changes will
*not* be saved (this is the `--rm`

flag). Directions are given in the file
on how to start conque with a screen …

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