# Build your digital profile as a data scientist

written by on 2020-01-04 | tags: career development job hunt data science

I have received questions from others on how to build a digital profile for career development. Everybody’s going to have a unique path, and what I think I can offer are observations on what I think have been helpful for myself and others who have enjoyed similar successes in getting started. My hope is that these pointers, which come from self-reflection over the past two years of work, help you.

## The maybe-timeless and cross-industry principles

First off, here are some things that I think are timeless principles in the job search, and are probably true for a large swathe of professional roles.

Firstly, nobody owes me their time or a job; I have a lot of leeway to make it as easy as possible for others to say, “Yes, I would like to have you on board”. Naturally, this statement ignores structural privileges and inequalities that exist in society, so I would be realistic in what I can accomplish. That said, it's usually a good prior to assume that everybody is busy, and that you need to properly advertise what you can offer.

In line with the same idea, I start with the presumption that nobody has the spare time to think deeply about a candidate when there are many other things in one's mind. The corollary is that it is on me to frame how I want others to view me.

## Data science-specific things

Now, for a data science role, being able to demonstrate the following can only help, not hurt, your application and candidacy:

1. Ability to use your skills to solve real problems of value.
2. Good coding practices.
3. Good storytelling and communication ability.
4. Proficiency with building "products" that aid in decision-making.
5. Ability to work collaboratively with others.

In data science, “value” usually means saving money. Saving time = saving money, in case that was not clear. Hence, automation is valuable.

Good coding practices are important: I have an essay collection on them, if you need a resource to get started.

Communication skills are universally important in professional roles, and data science is no different.

What does enhance communication is the ability to build interactive tools that guide a busy decision-maker towards ethical and profitable choices (hopefully in that order). Good value judgment is needed here!

## Where are prospective hiring teams going to look?

This comes from n=7 candidates for (I think) 3 candidate searches at work that I was involved in. The order in which I was looking was:

1. Resume
3. GitHub
4. Personal website
6. Old/current research group website

The resume is what you turn in. Make sure it's clean, readable, and that it concisely captures exactly what you're looking to communicate to the hiring team: how you're going to be a good fit for the role.

When I looked at LinkedIn, I was, quite interestingly, looking at their social network. Who might they plausibly know? At least for the team I'm on, I know credentials and certifications matter less than evidence of projects done, which brings me to the next place...

GitHub. I was looking for evidence of candidates' ability to code. A well fleshed-out GitHub profile with publicly browsable repositories and a contribution record that is mostly your own makes it so much easier to see your coding style. I also looked for evidence of familiarity with packages, continuous integration tooling, good version control, and collaborations with other package developers.

Your projects that demonstrate the data science skills above should be prominently featured on your profile page. Project types that, in contemporary times, communicate these skills well include:

1. Data products that you've built
2. Teaching material that you've made
3. Contributions you've made to other repositories, in particular pull requests and issues politely raised.

I looked at Google Scholar as well to get a flavour for a candidate's prior research work. It's an indication of one's domain expertise, and possibly an indicator of the kinds of problems one will gravitate towards. (This last point has been at least true for myself; however, for one jumping from, say, biological data science to flight data science, this will be much less relevant.)

The diversity of one's collaborators also helps paint a picture: did you specialize in work with one other person all of your academic career, or did you work in large teams, or did you work mostly solo? (Don't put a value judgment so quickly: each has their own strengths.)

A candidate's old research group is something I would check only out of curiosity, just to know more.

## The kitchen sink of tips

Tip #1: if you put your source code on GitHub, always include in the README why the repository exists, and a guide to how to use the repository. It is a marker of "sociable working style": in other words, you're able to think of how others are going to interact with things that you've created. (Using others' tools happens all the time at work!)

Tip #2: If you put a notebook up in your repository, be sure to make the repo Binder-friendly. It doesn't take much: environment spec file is all thats needed!