Tips for Data Science Job Seekers in 2023

So you want to get a data science job and need help figuring out where to start? Well, you're in luck because that's what I'm here to help you with today. Here are five job-seeking tips and tricks to ensure you're putting your best foot forward as you begin your job search based on my experience as a hiring manager, mentor, and educator.

Updated Resume

The first two tips are general job-hunting advice but relevant for you, the data science professional. If you do nothing else in your job search, reviewing and updating your resume is the most critical step you can take to ensure your profile matches all of your experience. Depending on your employment status, it may have been a few years since you've needed to submit a CV, or if you're a student, you'll need to write your first resume. Whatever the circumstance, make sure potential employers know what you've been up to -- including employer names, when you started and ended that role, your job title, and some bullet points detailing your primary job responsibilities. You'll want to include everything relevant to the field: degrees and certificates earned, internships, publications, completed online coursework, and professional trade organizations of which you're a member. As a data science candidate, you have specialized skills: enumerate them in your resume's technical competencies and knowledge section. It's also a good idea to have a short paragraph where you tell the story of your professional experience: this is your opportunity to control the narrative for recruiters and hiring managers. Ideally, you will immediately update your resume before your job search. Adding elements to your CV as you earn new skills, experience, knowledge, or certificates demonstrating what you've accomplished and how that experience is relevant to an employer is good practice.

LinkedIn Profile up-to-date

Everything I suggested about resumes applies to your LinkedIn profile. If you have a LinkedIn account, you're getting a medium to interact with recruiters and employers. As our lives become increasingly digital and play out on the Internet, it's vital to go where the recruiters, networkers, and employers are -- and I can guarantee you, your potential new company almost certainly has a presence on LinkedIn.

On LinkedIn, you'll want to include everything you listed in your CV -- in fact, LinkedIn makes it even easier for you by allowing you to submit your resume and auto-filling your profile sections with the relevant information. If you use this feature, double-check to ensure the information is correct and tagged in the appropriate area. LinkedIn has an About Me section, where you'll craft the narrative about your professional career to employers. There are additional features that are useful to you, the data science professional, namely the skills section. The LinkedIn skills section is excellent because you add your technical competencies as you build out your network of colleagues, classmates, and connections and endorse you for said skills. Endorsements are a quick way for recruiters to look at your profile to see that your peers or other knowledgeable industry experts can vouch for your capabilities. Finally, suppose you have professional references willing to speak about your experience and share a narrative about you. In that case, LinkedIn provides a feature to ask for professional recommendations, which will prominently display in your profile if this request is accepted.

GitHub/Portfolio

Now we're getting to the industry-specific job-seeking recommendations. Imagine you're an artist for a moment: you'll want a portfolio of your best work. The same applies to architects, musicians, writers, creative directors -- anything that involves unique individual contributions that provide a sampling of your work. But, despite your thoughts, you are no different as a data scientist. You've probably worked on passion projects, grad school work, or contributed to an open-source project: these are opportunities to show potential hiring managers that you can back up what you've listed on your resume or LinkedIn profile. So why wouldn't you want to demonstrate your programming capabilities in an easily consumable and organized manner when leveraging Git is a valuable skill in and of itself?

If you want extra brownie points, using GitHub Pages is a unique way to display your project portfolio in a more user-friendly front end -- especially if you have a background in UX. Integrating your machine learning or data science model into a hosted production application with an interactive front end will help you stand out even more with potential employers.

Hacker Rank/Kaggle

Another way you can demonstrate your capabilities to recruiters and hiring managers is by participating in a coding competition, such as Kaggle (data science-specific) or HackerRank. Large companies often sponsor Kaggle competitions with prize money at stake for the highest placing submissions, but more importantly, tracking your place among every competition entry on Kaggle. So naturally, the more accurate your models and predictions, the higher your rank. It's important to consider that Kaggle is not the end-all, be-all of data science acumen since the winning submissions often require highly tuned models and data engineering, which may not translate to other problems. However, you can view a high score for what it is: an accurate and creative solution to a challenging data science problem that demonstrates value to potential employers.

Attend networking sessions

Finally, if conferences, forums, networking, or group events are happening in your local area, it pays to attend and connect with like-minded professionals. You never know where opportunities will arise, so getting in touch with data scientists, machine learning engineers, and potential employers around you is a way to see if any local companies are hiring for roles that match your experience and career goals.

Conclusion

In conclusion, embarking on a job search in data science can be an exciting and rewarding journey. By following these five essential tips, such as updating your resume and LinkedIn profile, showcasing your work through GitHub or a portfolio, participating in competitions like Kaggle or HackerRank, and attending networking sessions, you can effectively demonstrate your expertise and passion to potential employers. As you put your best foot forward, remember that your unique skills, experiences, and creativity will set you apart in this dynamic and in-demand field; embrace the opportunities to connect with like-minded professionals and organizations, and you'll undoubtedly find the perfect role to propel your data science career forward.

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