The unspoken difference between junior and senior data scientists

By Mısra Turp, Data Scientist

 

Being a senior data scientists is treated as a holy grail, though many do not know what it really means to hold a senior position. The most common impression is that being a senior data scientist means that you know everything there is to know about data science and you are truly an expert in it. Which is true, but only to a certain extent because learning in data science never ends. Moreover, there is so much more to being a senior data scientist than just technical knowledge.

You might be thinking, yes, but why should I care just yet? I believe it is important to know the standard path data scientists follow so that you can make smarter decisions about which side of this path you want to be. Simply, the more informed you are, the easier it will be to choose your side when deciding between two companies, two positions or two projects.

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Let’s take a look at what a data scientist’s usual career looks like.

 

Junior Data Scientist

 
Background

As a junior data scientist what is expected of you is to have fundamental data science knowledge. Your capability should be enough to conduct your tasks alone or with help from your more senior colleagues. At this point in time, you will not have much professional hands-on experience.

Learning

You should be open to learning and not be afraid of asking a lot of questions. More senior colleagues will be happy to help you along with your learning. It would not be surprising if you learned something new every day as a junior data scientist.

Projects

Your main responsibility will be the tasks that are assigned to you. You will get assistance from more senior data scientists when you encounter problems. Apart from your technical capabilities, you will be expected to have a good understanding of the parts of the domain that is related to your specific tasks.

 

Data Scientist

 
After junior data scientist, you’ll likely be in a transitional role where you would be called plainly: a data scientist. ‍

Background

At this point, your knowledge of the main concepts and techniques of data science must be solid. Though it doesn’t mean that you already know everything. Rather, it means that you know many things and you also know what you don’t know. You probably have already gained some good practical experience at this level.

Learning

Learning never ends so you’re still open to new ideas and approaches. You still ask a lot of questions but you also get asked questions from others. Junior colleagues come to you with their questions. You still learn new things, maybe not every day but every other month. You try to go deeper in our understanding of certain techniques and tools.

Projects

You are a part of the decision process in projects. You have a good overall understanding of the context of the projects but you still don’t need to know more than what you need to do your job with.

 

Senior Data Scientist

 
And then comes the senior data scientist position. You are basically everything a data scientist is at this point with some extra capabilities and responsibilities. Let’s see what they are.

Background

You have a solid understanding of main concepts and techniques and also a deeper knowledge of their pitfalls. You gained this knowledge while working on projects. Now you have a solid level of practical experience under your belt.

Learning

It is easier for you to learn more advanced topics because you’ve already mastered fundamental concepts. You are still open to learning. Teaching and supporting more junior colleagues is part of your job.

Projects

You are the leader in projects. You are not only part of the decision-making process but you lead it. The success of the project is your responsibility, as well as, in many cases, the happiness of your team members. While leading the project you also need to communicate with the outside world. It is your responsibility to report to the business side. You need to keep non-technical constraints in mind while working on the project and make sure to nudge the technical team in the right direction. You have to have an overall and complete understanding of the context and the domain. It is your responsibility to stay on target and deliver.

 
Of course, this is not how every single data scientist’s career goes in every company in the world. Also, you might be a freelance data scientist or you might start your company and become a CTO, then your path would look very different. But in general, from what I’ve learned talking to people in the data science community, this is a good representation of a general data scientist career path.

The reason we looked into this today is that every company has its own structure, own rules and own pathways and you want to know which one to choose when you get the option. Some will tend towards more technical work as you get more senior and some towards more managerial and administrative work. You can use the explanation in this article as a baseline to figure out where you want to be at the senior part of your journey and calibrate your job search accordingly. Of course, plans and preferences change in time. But having an idea of where you want to end up is better than going into it blindly.

Do you already have an idea which way you want to lean towards? Let me know by commenting or shooting me an email!

Check out So you want to be a data scientist? for articles and free resources specifically for the busy professional looking to transition their career into data science.

 
Bio: Mısra Turp: I am a data scientist by day and online creator by night. Currently I work for a pharma-tech company called myTomorrows in Amsterdam, the Netherlands. Before that I worked as a data scientist at IBM.

Apart from my data science work, I like teaching people what I know. I always received lots of questions on LinkedIn about my career and data science in general from people who wanted to end up where I am. That's why I started So you want to be a data scientist? That's where I share my knowledge and experience through my articles and access to my connections through my podcast.

I made this course after I realised that the biggest deficiency of aspiring data scientists is practical experience. I designed this course to help you bring together all the little bits of knowledge you acquired so far and along the way build a project in your hands you can be proud of.

Original. Reposted with permission.

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