We are currently building out our data team. And that means hiring across the three broad areas: data science, data engineering, and data analytics. But as I have been reading resumes and conducting interviews one thing that has stood out to me is how variable it is to assess where one is at in the spectrum of experience. And yes, it is often gendered (women tend to think they are less senior then they are, men tend to think they are more senior).
With that in mind, titles within the tech industry can have wildly different meanings. The title Chief Technology Officer (CTO) at a 3 person start up means something completely different than a CTO at a 200 person company, which means something completely different at a 2000 person company. The role changes, and therefore the title has a completely different meaning.
One of my favorite conversational topics is discussing the meaning of words. For example: the word “smart” has a fairly subjective meaning. Does “smart” mean you know things? Or that you can win a pub quiz? What does it mean to be “smart”? When I point this out to people they are often flabbergasted.
But words do have meaning and I think defining them is often really important for clear and transparent communication. While the title of this article seems to indicate I’m going to focus on the word “senior”, I’m actually going to take a more holistic view. I’m going to look at the two ends of the spectrum of experience that we’re currently hiring for in the data field at Devoteam Creative Tech: junior and senior.
But before I dive in: we are big proponents of finding good people and then finding them a role where they will succeed! As a result the following is a guideline and we encourage you to apply, regardless of your level!
Junior means you don’t have experience in your chosen field. Which is totally fine, everyone has to start somewhere. When i’m looking for a junior position I want people I can invest in. Which means I want 3 things from a junior hire:
1) They are able to quickly pick up new information and synthesize it with what they already know (btw this is my definition of “smart”).
2) They are excited about what they are doing.
3) They are willing to admit when they are wrong/have messed up.
If you have those three things, I can teach you anything. I will often ask candidates during an interview “tell me about a time when you were wrong”, just to test this last one. So if you have no experience, don’t worry as much about doing side projects to build a portfolio. A good company will want to invest in training you, if you are trainable.
Years of experience aren’t everything. People develop at different rates, and someone who hasn’t been pushing themselves to learn is going to have a longer time to get to the senior title than someone who is thirsting for knowledge. Additionally: a degree does not make you senior. Trust me, I have a lot of degrees.
So what does it mean to be a senior? No matter which discipline you are in, it’s going to mean the same thing: you need to be able to complete a project end-to-end without much supervision. For each of the subfields of data, the concept of end-to-end varies.
In data analytics this means you need to know how to define the business problem with your stakeholders, as well as knowing how to find insights from the data. You need to be very familiar with the product, whether that be the marketing campaign or a new feature your software team is developing. And you need to be able to teach a class in SQL, because you are able to quickly use this tool to take data and turn it into gold.
In data engineering this means taking data from the product and putting it into a data warehouse. You need to be able to do this with an eye on the future (how will this data change) and thoughts of quality (is this data clean? Will the people downstream be able to use it easily?). And you need to work closely with analysts, data scientists, and engineers in the project to make sure that the data you’re collecting is the data that is relevant and needed.
And for data science/machine learning you need to be able to build and deploy a machine learning algorithm. That means collecting the data from the tables in the data solution, training a model, evaluating the model, and then deploying it so that it is decisioning/predicting/recommending within the product you build it for. Each one has slightly different requirements, some of which will vary from project to project. But if you’re a senior, I should be able to put you on any project and you will quickly be able to contribute to any step in the “end-to-end” pipeline.
Being able to complete something end-to-end without supervision doesn’t mean hiding when you are struggling! Or being afraid to ask for help when you encounter something you haven’t seen before. Both of those behaviors are actually more indicative of a junior mentality. The big difference is that before you ask for help, you should be able to figure some things out yourself so you’re asking better questions. There are no bad questions, but there are good and great ones.
Also, I touched on this before, if you are coming from another industry and you don’t have any experience in the field you are entering then you are not senior. You can’t be. I used to be an academic and I transitioned into industry ~ 5 years ago, so I know what I mean when I say this: you don’t know all the things you don’t know. I keep seeing people who have 0 experience outside of the academic field think they are ready for a senior role. And in some companies that might be true. But here’s a little secret: if you don’t have any experience outside of academia, then don’t be the only data scientist at a company. Even one that will give you the senior title. They are giving that to you because they don’t know enough to know what you should do either.
Instead, you should go somewhere that has people who can complete a project end-to-end, then ask them to teach you everything. If you are the smartest person in the room, then you’re in the wrong room! Skipping the bigger title for a place where you can learn more will pay off in spades over the course of your career.
With all that in mind, when I’m hiring I’m looking for curious people who want to learn from their mistakes. One of my personal catch phrases growing up was: “Take chances, make mistakes, get messy!” and we’re looking for people who want to jump into the mess. And that’s true at any experience level.