3 skills a data scientist needs to set them apart from the competition
We’ve all heard about the strong demand for data scientists in the global job market, but if there’s a job you’ve got your heart set on, you don’t care about aggregate numbers.
You want to set yourself apart from your direct competition, and for every desirable job there will always be competition. What are the three biggest things that are going to set you apart?
To answer this, I began, like any good data scientist should, by gathering some data. I surveyed articles by hiring managers and thought leaders on skills needed to be a data scientist and get hired. Here’s what everyone expects of you.
These are the table stakes of being a data scientist.
● A foundation in statistical principles
● Experience in machine learning methods
● Coding abilities in select languages and select packages
● A long and varied list of other technical abilities unique to the role that typically require experience with systems and tools for data storage, transfer, and processing
Non technical traits:
● Intellectual curiosity
● Business acumen
● Communication skills
● Problem solving
If this is what is expected, what will set you apart from the competition?
#1 Deep learning experience
Building models based on advanced, large neural networks (aka deep learning, aka AI) is all the rage in today’s data science marketplace and for good reason.
These models show a lot of promise. They often maximize predictive power, and they can be applied in a variety of industries. They are reusable by nature as evidenced by the increased attention given to model zoos and transfer learning. This field is changing rapidly.
New tools and new theories for optimizing networks constantly emerging. When employers hire someone with a background in both traditional machine learning methods and deep learning, they help future-proof their team’s skill sets. They can ensure they are not falling behind their competition who may be using deep learning. They will also view you as a resource to help up-skill the rest of their team.
#2 Results-focused approach to your work
How would you answer this question in a job interview?
“What do you enjoy most about doing data science?”
Is it to feel the thrill of iterating to the optimal model? Is it the challenge of research? Is it to try out a new package and expand your knowledge? If this describes you, then you are just like the rest of the candidates. You will not stand out.
In order to stand out you need to fall in love with making a difference to the bottom-line. This doesn’t mean you have a cold, sterile, money-talks view of the world. This means you genuinely want to know what difference your model is going to make in the world. Who is going to consume it? What is it they are trying to do that could benefit from your model’s output? How are they going to interact with your model?
I call this being results-focused. When you are results-focused it affects everything you do as a data scientist, from the way you prepare before modeling to the metrics you use to determine success at the end of the project. Most employers don’t invest in data science for the love of research. They want to see results. If you show that you share that same world-view, it will set you apart from the competition.
#3 Demonstrated love of learning
I’ve been in this space for some time now. I’ve seen trends come and go. Hiring managers have usually been around a while too and have seen the same thing.
You know what you know today, but are you willing to put in the effort to stay up to date and be a valuable asset for tomorrow and beyond? If you can show solid examples of your love of learning, you will stand out. This can be in the form of certificate programs to round out more formal education, diving deep into a research topic demonstrated by speaking about it at technical conferences, or being aware of current trends and new analytical packages.
Additionally, a few examples in your work history of taking on complementary support roles such as managing products, owning marketing tasks, or being a liaison to business teams will show your passion for expansion. Let them know that your resume is a living entity and is always subject to change.
In order to be a great data scientist and to land the job you want, you have to think about what your employer is looking for. You have to consider why they are investing in data science in the first place. When you have deep learning skills, you help ensure they won’t be lapped by the competition.
When you are results-focused, you ensure that your contributions will have a real impact on the organization’s goals. When you demonstrate a love of learning, you show that you are a safe bet for today’s changing analytics space. As you help your future employer see these traits in you, do so with passion.
Being a data scientist is a great job. Show your passion. Have fun. Stand out.