Getting Noticed in the Job Market

The data scientist role is a popular career choice for anyone who likes to work with numbers and analytics. Once referred to as “the sexiest job in the 21st century” by Harvard Business review, the popularity of this industry has caused it to become oversaturated with job seekers and bootcamps.

With so much interest and competition, it has become harder for aspiring data scientists to stand out and get noticed in the job market.

What can aspiring data scientists do to stand out?

First, it’s important to understand the reality of the data science role, particularly what it entails for junior and entry level positions.

The data science job title has become an umbrella term for anyone that works with data. A lot of these roles will be very analytical in nature — pull data, analyze data and share results. Others will be more focused on data quality and accessibility — build ETL pipelines, monitor data integrity and service level agreements. Few junior positions will deal with model building and machine learning.

Is this reality going to be a deal breaker for pursuing a career in data science?

If not, then great. Make sure you understand what type of role you are applying to and which skills will be relevant for this role. Cater your resume to speak to these skill sets. For instance, a business analyst may have experience analyzing and generating insights from data. Even though this is not a “data science experience”, it is still incredibly relevant for many data science positions out there.

Here are some ways you can make your profile stand out.

Tangible projects to show you can apply your knowledge

We’re fortunate to have access to a wide range of tooling to make model building and mathematical calculations easier. With this tooling, we can be more lenient when assessing the theoretical background of our candidates, especially the more junior ones.

Aspiring candidates should focus on their ability to apply their theoretical knowledge to build data products that would be useful in the real world.

Here is a small checklist for good side projects:

Am I using the right frameworks for this project? Are they relevant in today’s industry, and do they make sense for this particular application?

Take a Python project as an example: you have the de-factor libraries such as TensorFlow and Scikit-learn for most of your modeling needs. These libraries have become popular for good reason. If you are deviating from these standards, you should ask yourself whether your choices make sense. This is important so that 1) you don’t reinvent the wheel when you don’t need to and 2) the skill sets you are demonstrating are relevant.

Am I demonstrating good software engineering practices? Is my code testable, maintainable and interpretable? How reproducible is my work?

Always make sure that you are writing code with an audience in mind — is it easy for someone else to contribute or review your code? Is it easy to understand your code after several weeks? Set up instructions should be clear regardless of what machine they are using.

Am I demonstrating strong problem solving and communication skills? Is it clear why I made these decisions?

Problem solving and communication are two key skills to being a successful data scientist. Being able to communicate your ideas (especially the why component) is a distinguishing factor between a good scientist and a tool like AutoML.

Build a strong online presence

Once you have a suite of work to show, make sure that it is easily accessible. It is highly recommended that you share your code publically through a platform such as GitHub. This will make it easier for prospective employers to find and navigate through your work.

Make your online presence known. Maintaining a technical blog where you share ideas and learnings will supplement your resume and make it easier for your work to be discovered. Consider building a personal website as well to further showcase your passion and technical skills.

Be active in the community. Contribute to open source projects whenever you can, whether it’s opening an issue that you experienced or making small improvements to their codebase.

Join a community or two

There are tons of online communities for data science practitioners. Here's a list of Data Science and Machine Learning Slack communities.

Also keep your eyes peeled for local communities, whether online or in person (meetup.com is a good place to start!). These communities will often host events in your vicinity with local professionals and employers. They are a great opportunity for networking and job hunting.

Continue to learn and build your toolkit

A data scientist wears many different hats. There is an incredibly wide arsenal of skills you need to have to be successful in this role. The industry also changes very quickly and requires a lot of learning to keep up with these changes.

Never stop learning, and never stop growing.

Stay on top of these changes by fostering a propensity to learn. Acknowledge your strengths and weaknesses and create actionable plans to continuously improve yourself.