Finding a job is hard.
Finding a job when you’re starting out in (or transitioning into) a new field, especially data science, can be even tougher. In this post, I’ll provide pro tips for how to get hired as a data scientist, based on my experience as a career advisor and recruiter. As a Career Advisor at Metis Chicago, I helped multiple cohorts of students find their first data science jobs and gained significant insight into the process of getting hired.
To start, let’s cover a few basics:
Job searching is largely digital nowadays, which makes it incredibly important to be searchable online. Create a LinkedIn profile if you don’t have one already, and be sure to stay active on it. Additionally, in data science, maintaining a GitHub repository is a huge plus, and so is having a personal website and/or blog focused on data-related topics. Within this post, I’ll go into some specifics about how to fill your new GitHub and blog, so read on for those tips. In the meantime, simply establishing a digital presence is a great first step.
Once you’ve set up those sites, it’s time to start thinking about your P.L.A.N. (Practice; Learn; Apply; Network.)
Start building some stuff! You need to practice your data science skills to keep them sharp. This will help you be better prepared in interviews. Committing to demonstrating your progress on your GitHub and/or blog is also a great way to gain attention from recruiters. During a full-time job search, aim to spend about 5-6 hours per week practicing your skills. A few great ways to spend your time include:
The world of data science is constantly changing and new technology becomes available all the time. You won’t be able to know everything by the time you start applying for jobs, but you should try to stay up on new trends as much as you can. Many employers are looking for someone who is curious and has a thirst for learning, so be sure to dig in and demonstrate what you’re learning! I love the following learning resources and have witnessed them benefit many Metis graduates:
Have you ever found yourself filling out a job application, hovering over the “Apply” button, closing your eyes, clicking send – and then just hoping for the best without further action? We’ve all been there, but there is a better way!
Who can relate to the image above when thinking about networking? Pretty much everyone! You’re definitely not alone if you get nervous about networking with strangers. But think of it this way instead: networking is just meeting individuals with similar interests and talking about cool stuff. Plus, there are very tangible job-related benefits.
Find nearby events that look interesting (Meetups, university events, volunteer opportunities, etc.) and aim to go to at least one per week. While at these events, make it a goal to strike up conversations with 3+ people you don’t know, and connect with the speakers/hosts, too. Afterward, connect on LinkedIn and follow up. At the event, ask open-ended questions, practice active listening and share your story and goals when appropriate.
Important Networking Reminder: Data scientists are normal people! You are every bit the same caliber “talent” as they are (they have just spent more time doing it). Not everyone will be helpful or nice, but the ones who are can help big time.
It’s important to keep yourself accountable after your P.L.A.N. is out there in the world. Create a goals-driven calendar to stay on-track and on-target. Here’s an example of a simple one:
Talk through your solutions because an employer wants to gauge:
The job search can be a roller coaster ride. There will be ups and downs, and some weeks you’ll be busy as all heck, while other weeks may be quieter. But it’s important to stay positive and persistent, because it’s inspiring to work with someone who is motivated, exhibits grit, and has a good attitude. Remember, it’s normal to feel frustration, but if you incorporate the above strategies, I’m confident you’ll be able to push through that wall to the ultimate win: the data science job offer!
This article was contributed by Ashley Purdy, Metis Career Advisor, Chicago
This post is based on a workshop that Ashley Purdy gave at Metis’s recent Demystifying Data Science online conference. If you’d like access to all recorded talks and workshops (22 in total from speakers across industries!), register here and receive them via email for free.
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|Subjects:||Data Science, Python, Data Visualization, NLP, Unsupervised Learning, Regression, Web Scraping, Naive Bayes, Hadoop, Spark, Machine Learning, Git, GitHub,|
Metis's accredited data science bootcamp is a full-time, in-person, 12-week program. They offer a $3,000 scholarship for women, members of underrepresented groups, and veterans. The program involves 2-3 hours of classroom work and 4-6 hours of project and development work per day. Each cohort culminates with a presentation of capstone... Read More