I’m asked all the time, on places like LinkedIn and Quora, how a newbie data scientist can kick-start their career in the field to get noticed and get hired. I’ve responded in a number of different ways in the past couple of years, but now I’m confident the best strategy to stand out in the crowd is to share your work and let colleagues and future employers see what you’ve done.
There’s a big shortage of data science skills right now. That’s great because you can take advantage of this inefficient market. There’s one small problem. Competition for available positions is fierce, especially if you are just starting out and don’t have years of experience to sell your case to prospective employers. So, how do you stand out and get your foot in the door of this dynamic industry? Easy answer – market yourself and build your personal brand.
In order to gain a foothold in the field, consider taking advantage of all the cool resources available to data scientists these days and use them to highlight your growing body of work. Let me offer some suggestions to help get you started.
The famed data science challenge site Kaggle is a great way to show off your work. You can publish compelling and high-quality kernals in order to demonstrate your command of the data science process. Here is a particularly good example of what one Kaggler put together, “Machine Learning Workflow for Housing Prices.” Kaggle also has a number of very active discussion forums where you can get your name out there by asking well-crafted questions, and maybe even try to answer a few yourself! Before answering, you can do research to come up with useful suggestions. It’s satisfying when other people start referencing your answers.
Share Your Code on GitHub
Use GitHub to share your machine learning code so others may assess your talents. Employers often look at GitHub repositories (repos) to investigate a candidate’s published work. Your degree of activity on the site (the green colored “contributions” heat map associated with your profile does wonders to attract attention) also is something to use as your personal merit badge. Always include a link to your GitHub account on your resume, as it can be an asset that helps you stand out in a crowd of applicants. You can put together a public GitHub repository where you keep all your Jupyter notebooks.
Publish Your Slide Decks on Slideshare
It’s a great career development idea to join one or more local Meetup groups with a focus on data science, machine learning, deep learning, R, etc. Once you get to know some of the members of the group, you’ll feel more comfortable discussing important technical topics. I know for a fact that Meetup group organizers are always looking for people to make presentations to the group. Be up front and make it known you’re a newbie, but that you’d like to share your thoughts on a technical matter. Pick your topic carefully and research it fully when putting your talk together. People will cheer you on and you’ll gain respect by taking a chance like this. Once you’re done, you can publish your slide deck on a repository like Slideshare. LinkedIn SlideShare is a hosting service for professional content including PowerPoint presentations.
Leave Well Thought Out Responses on Stack Overflow
Stack Overflow is arguably the most respected technical resource for data scientists. The site’s discussion forums are commonly the most cited in Google searches for a broad range of technical questions. Create an account and search for discussion topics you know something about and leave enthusiastic, supportive, and polite responses containing your technical thoughts. You can establish your own personal data scientist brand by frequenting Stack Overflow.
Answer Quora Questions
Quora is a Silicon Valley-based Q&A website where questions are asked, answered, edited, and organized by its community of users in the form of opinions. There is strong coverage for the data science topic. I contribute all the time when people pose questions for me to answer. It’s fun to share insights. Get people to ask you questions on topics for which you’ve demonstrated good knowledge.
In summing up, my advice to up-and-coming data scientists is to break into this industry by having the mindset to never stop learning and always share what you learn. You can’t advance in the field if you do amazing work and nobody knows about it. It may be risky, but try to put yourself out there, sharing with the community what you know. It’s the right strategy for success as it benefits both yourself and others. It may feel perilous, I know, especially if you are just starting out and don’t feel like an expert. But working through uncomfortable feelings can lead to substantial dividends and this process is where professional growth occurs. Best of luck with your data science career!