Operate outside your pay grade

I have heard before rumblings that one shouldn't deliver more value than what we're paid for. Grumbles that sound like, "It's just a job", or, "The company won't take care of you."

As a matter of career advice I would give to someone else, though, I think operating outside our pay grade is what we ought to be doing.

Operating outside our pay grade means both going above what we are paid to do, to get a better/higher contextual view of what we're doing, and moving sideways to do adjacent things beyond our role. (That is where "above and beyond" comes from, I guess.)

What does "operating outside of our paygrade" mean? I think the following:

  1. Taking projects from conception to completion, so that you can build that portfolio of stuff done. (see: Build a project portfolio)
  2. Mastering something rare and valuable.
  3. Constantly cross-training (see: The importance of cross-training) to learn adjacent skills (see: Learn adjacent topics) and complementary ones.

So operating outside of our paygrade really means mastering adjacent skills in pursuit of being able to own a project end-to-end.

Pages that link here

State of Data Science
This was inspired by my participation in the TAO Data Science Panel

Build a project portfolio

Eugene Yan writes extensively on the topic of data science careers, and I particularly enjoyed the essay he wrote titled Why Have a Data Science Portfolio and What It Shows.

A tl;dr summary of what he has in there:

  • The "why" is more than "getting a job". The process of building the portfolio matters more.
  • The process involves developing qualities: persistence, continual learning, altruism (to help others).
  • The "what it shows" includes both technical skills and those aforementioned qualities.

And a notable quote:

IMHO, traits and skills are a prerequisite to building a great portfolio. And they reinforce each other.

Also:

A portfolio is just an artifact of our skills, traits, and working process. It’s the destination; it’ll take care of itself if we focus on the journey.

Reflects very much the story behind The Score Takes Care Of Itself, by the legendary NFL coach Bill Walsh.

Learn adjacent topics

When picking out a topic to learn, I think it makes a ton of sense to learn adjacent topics to what we already know, i.e. things that have moderate degree of overlap to our existing knowledge base.

I think a moderate degree of overlap is necessary. If there's too much overlap, there's little expansion of the mind that happens when we learn a new thing. If there's too little overlap, there's too big of an entry barrier into learning that new thing. Having a moderate degree of overlap helps.

The importance of cross-training

This afternoon, as we were watching a singing contest, I found myself being superbly impressed by contestants whom, apart from having great voices, also had a great ability to present themselves. (One was a theatre performer as well.) These cross-trained contestants were able to subtly tweak their performance according to their extra knowledge that, when added together, amounted to excellence made on full display.

This made me thing about the importance of cross-training. Cross-training in a second discipline can help enhance our abilities in our main discipline. For example, for a data scientist, having some amount of domain expertise, or having the ability to very quickly pick up a new domain, can help focus the work that we do on the problems that matter the most in our domain.

An essay on Forbes also makes the case that cross-training can be great for an organization as well. For an individual contributor, cross-training colleagues on my own own duties forces me to possess clarity in purpose and process, which will level-up my own skillset as well. Additionally, as a team, we become more resilient as an organization when we are able to cover for one another.

State of Data Science

This was inspired by my participation in the TAO Data Science Panel.

I'm starting to see a bifurcation in research vs business data science.

How this translates to training needs and hiring

And notes for managing data scientists: