Data science is touted as the hottest career option in the 21st century. Being recognised in your field can make you more valuable in your current job and more marketable too. But, even after having all the packed-up arsenal and necessary skill sets, many are not able to make the mark to get hired as a data scientist. This happens usually because of the lack of visibility and exposure that they are not able to get in the job market.
In this article, we will explain in detail certain tips and tweaks that is a must for every aspiring as well as mid-career data scientists to gain more visibility and exposure in this heavily competitive market.
1| Improve Your LinkedIn Profile Along With Your Usual CV
The first and foremost mantra is neatly curating your LinkedIn profile with valid info.
- Update your LinkedIn status daily to bring more visibility to the recruiters. Fill out the summary section. Upload a good photo. Add your location. Include your educational background. Specify your industry. List your current position. Don’t forget to add your specific data science skills.
- Focus on your skills, experiences, education and achievements. Use keywords and phrases specific to the opportunity you seek to make sure you have a professional LinkedIn profile, ensure that what shows up in your social interaction reflects your personality and what you want to portray to potential employers.
- Recruiters are now familiar enough with many data science-based MOOCs to instantly recognise projects that are part of a standard course. So, do not mention these credentials on top but supplement these in the bottom.
- Create a CV that should fit on one page. Include any relevant honours, skills, and references. Only include experience that is applicable to the position you are seeking. The right CV should accurately summarise your experience in the tech industry so, far. This can include internships, degrees, and past employment. Sprinkle keywords heavily throughout your CV.
- Linkedin ProFinder– Use this feature as it connects you with strong leads based on their keyword searches and companies followed.
2| Join Data Science-Based Network Circles
The second mantra is to create a strong network base.
- You should engage with and join good data science-based associations and groups within your field. This could be a good source for getting referrals and to get in contact with hiring managers when a job is posted.
- Look for mentors. Find someone in your field who can provide mentoring and guide you in the field of data science and analytics. Ingrain yourself In your industry’s local community.
- Sometimes, job opportunities are showcased in and around certain network circles only. Many jobs don’t get advertised, which leads to this thing called the hidden job market. So, you must tap into this through networking.
- Go to talks, functions, and lectures that revolve around the industry you’d like to break into. Meet peers at various levels of experience, and naturally, let a rapport of camaraderie build as your professional network strengthens.
- References can make a significant difference when getting hired. References are important, and employers check them. Get recommendations from your CEOs, heads, colleagues and clients and juniors. Mention them on sites like LinkedIn and share them whenever needed.
- Mentor or advise student groups or start-ups. Advising such start-ups is another way of gaining exposure and contacts.
3| Create Your Portfolio
The third mantra is to increase your social media presence.
- Manage and optimise your social media presence through your Linkedin, Facebook, Twitter and other platforms to promote yourself. Recruiters today use all these platforms to see what potential employees look like.
- Follow Targeted Companies’ Social Media Accounts. Subscribe to weekly newsletters of the major technology companies that are hiring data scientists.
- Create your data science portfolio as it demonstrates that you can do the things that an employer wants to hire you for. It works as an effective substitute for the job experience if someone lacks.
- Any strong data science portfolio is made up of several medium-sized data science projects, that combined demonstrate to the employer that you have the key skills that they’re looking for. Highlight the practical problems with the data that you solved in the process of getting the insights and results on Github or Ipython Notebooks.
- If you linked them anywhere on your resume, cover letter, or on your LinkedIn page, be sure these sites are accessible and up-to-date, reflecting your most recent and best accomplishments.
- Recruiters often check your digital footprint. So, you should properly align your profile on social and professional networks with your current resume.
- Write for professional publications like for example KDNuggets, Analytics India Magazine, Analytics Vidhya, Tech Republic etc. Nothing beats seeing one’s name in print, with a by-line following the title.
4| Participate In Competitions, Summits and Attend Job Fairs
The fourth mantra is to engage in all the professional activities one can.
- Job Fairs put a bunch of companies at your fingertips. You get ample opportunities to get hired by these companies and should treat these interactions with a recruiter as a mini-interview.
- Follow discussion forums like Quora data science or Reddit and participate in data hack competitions on Kaggle or hackathons conducted Analytics India Magazine and should regularly present papers at a local data science meetup.
- You should participate in data science related volunteer work. You get exposure to data science and data science people by participating in some data science hackathons and/or meetups. See devpost.com, meetup.com, eventbtite.com, etc.
- By participating actively in such Hackathons such as Machine Hack, Kaggle competitions, coding competitions and open source projects to practice coding R, Python, MATLAB, not only will you get exposure, but you stand higher chances to get selected in interviews as companies look for problem solvers.
- Focus on a specific data science area for some time and publish blog posts and codes on it. It will convey that you can do larger and structured projects on your own. It will make more sense than uploading every small piece of code that you write too. Use RMarkdown or Project Jupyter or others to create documentation for your projects which makes it easier for others to see what you have done.
- After finishing add-on courses/MOOCs on data science from platforms like a coursera or Edx, participate in competitions and replicate the results as interesting papers in the data science literature as it will make you look much more unique, gives you cutting-edge work to showcase and discuss during interviews.
5| Register On Curated As Well As Data Science Job Portals
The fifth mantra is to create your profile on industry-specific job portals.
- Search for specific data science job titles in search databases. You can register on all the available official online job portal such as LinkedIn or Monster Jobs or Glassdoor, Indeed and specific data science job portals like AnalyticsIndiaJobs which gets you job openings specific only to data science and analytics across major cities in India.
- You should subscribe to job alerts on your targeted companies’ websites, popular job sites and recruitment agencies too.
- Learn the art of the cold email and share your website/blog to your potential employers consistently to gain attention with respect to the projects you have done to date.
Foot Note –
When recruiters look for data scientists, they look for people with a passion who want to build great things. They look for indicators that tell them that the aspirant is committed to action and not just theory. The major focus is on the tech skills that they possess outside of their work.
Also, whether they understand the current businesses nuances and challenges of the working environment and how quickly can they deploy data analytics to solve issues. Whether they are able to craft a story from a data set and communicate the key data findings efficiently. Along with technical skill sets one holds, the communication pattern is one of the primary things that a recruiter looks at, for which the way you connect, engage potential recruiters through your portfolio a, social media presence and active participation in competitions and varied summits ends up as a primitive factors to get hired as a data scientist.
If you want to understand the hiring game in the world of data science better, you can check some articles here.