Have you ever wondered why some individuals from the same class or discipline get better jobs than the others, sometimes despite poorer grades?Well, that’s because they are “smarter”. They have leveraged their capabilities and skillsets (big or small) in a “smart” way that got them jobs with great salary packages in fast-growth companies. So as you near completion of your Data Science course, and set sail on the next stage of your data science journey, be “smart” about your job application process. This is your ultimate step forward that will propel you into the data science cosmos. While your course or institute may be your launchpad, it is the manner in which you launch yourself that sets you ahead of your peers. What may that be?
Although there is no set format or hard and fast rule, data science job applications usually call for more rigor than most other job applications. Why so, you may ask? That’s because your data science application calls for a supporting portfolio. Once you have navigated through the process required for a data science portfolio, it is time to shoot those data science job applications.
It is not enough to have good grades and excellent projects in your portfolio to get that data science job you have your eyes on. There are many other aspects that need equal consideration when you are applying for a data science job.
However what nobody will tell you about a data science job application, is that you:
- Need to have a clean timeline in your social media accounts
- Have to keep promoting yourself
- Keep expanding your LinkedIn network
- Search for companies and data science job roles using the right keywords
- Should research companies you come across
- Avoid generic job hiring websites
- Leverage connections
- Include a cover letter in your job application
- Follow these resumes Do’s and Don’ts
A. Check your Social Media Accounts
Almost everyone has more than a couple of social media accounts. You may have joined Facebook, Twitter, Instagram, etc., because of an invite from a friend. However, it is time to revisit your priorities. While social media activity is a great and healthy way to hang out with an online community, catch up with friends and colleagues, rant and rave about your favorite cricketer and pop star, or be vocal about your political ideology; they reveal more about you that you’d normally think. However, it always makes good sense to separate the personal from the professional. Whether you include your social media profile in your resume or not, sometimes social media checks are part of the hiring process, especially if the company handles sensitive information.
So check your social media accounts. Are they visible to the public? Do they contain any strong statements against a former company you worked in? Are you in the habit of posting personal images that border on the objectionable? Does your twitter handle to display your name? Do you follow any banned organizations or porn stars? Look at your social media accounts from a third perspective. What impression does it convey?
Use a Python library to clean up your social media for any content that may work against you. Or make them “private” and anonymous.
Include your social media handle/page only if it is professionally geared. Hirers always like to see their employees engage in dynamic conversations with the data science and developer community. Sometimes this is how you may come across a client or a team member who can help you solve that tough problem.
B. Keep promoting yourself and your work
The three mantras of self-promotion are to act, react, and share.
Opening a GitHub account and posting your projects is not enough. You have to keep promoting yourself, your projects and your capabilities. Be consistent. An intense activity over a month, followed by a period of silence or non-response to the queries and suggestions you receive, is an absolute no-no. Be prompt in responding to questions or feedback. Keep a sustained effort at self-promotion. Say, set aside certain days every week when you focus on being proactive in the public domain. Maintaining ongoing visibility is a great way to make you known as an active and vibrant member of the data science community. Share your ideas, offer solutions to problems put out by others, and contribute to the larger domain of data science.
C. Expand your LinkedIn Network
Linked is a proven professional network, where you will come across data science professionals, companies hiring data science job roles and content related to your career and learning. It is a great platform to post blogs or comments on posts with robust suggestions that display your problem-solving mettle. Expand your network to include members of the data science community from across the world. Look for companies in the target location. Use the appropriate keywords to discover companies deploying data science. Follow them. Enable job notifications.
D. Search for Companies and Data Science job roles using the right key words
While LinkedIn has a system in place which makes your searches easier, the web is a wider arena, calling for the right “search” skills. If you are a data mining or scraping expert, it will come naturally to you. However, some of you may be a data science newbie and a greenhorn about the job search.
Make a list of your skills and data science job roles you are keen on. Search using these keywords in the target geography or location. Filter using your own preferences. Draw up a list of companies you would like to apply for a job.
E. Research companies you come across
Now that you have a list of companies that deploy data science or are actively looking for any of the data scientist roles, you find more. Research the company to understand how fast and deep is their data science implementation. Will the company offer you a thriving environment for fast career growth in data science? What are the job roles the company has advertised the most? Is the company more focused on Machine Learning and Deep Learning, or are they looking at architecture and big data analytics based job roles?
What is the industry? Does the industry and domain interest you? Have any of your projects dealt with problems in that specific domain? Can you see yourself growing in that industry? These are some questions you must ask when you go deeper into company profiles.
However, very often the company profile may not reveal future plans in product or service offerings. So a good way is to check out media reports to know where the company is headed, and what plans they may have in the offing that match with your plans of a data science career trajectory.
Researching companies, and learning more about their ongoing projects or future strategies, also prepares you for the data science interview.
F. Avoid generic job hiring websites
You must know that generic job hiring website use applicant tracking systems. If you do not use the right keywords in your resume, you may be opted out automatically. While it makes sense to submit your resume on such sites, it is foolish to depend upon them.
Very frequently, a company’s hiring process does not reflect the “want lists” of the engineering teams, and HR is not as proficient in separating the wheat from the chaff while screening through hundreds or maybe thousands of applications.
HR screens for credentials, and whether you attended a good school or worked at a good company. HR isn’t equipped to sniff out technically talented applicants who may not have a great schooling background but is a coding gem.
G. Leverage connections
Scoring 500+ connections on your LinkedIn network shouldn’t be your ultimate goal. Treat platforms like LinkedIn as an important touchpoint in your data science journey. Check out which company is hiring, who is engaging in a new project that you feel you can add value to.
They may be connections on your Twitter, Instagram or just like-minded people you met at an engineer-driven bootcamp or data science meetup. Get together. Bounce ideas, exchange job application stories, discuss companies, and emerge enriched with all that information. Who knows, you may have just networked with your potential teammates, boss or clients?
H. Include a Cover letter in your Job Application
I have observed that the millennial and Z Gen are not too fond of sending cover letters. However, a cover letter often makes you stand out among the herd besides being personalized. A brief cover letter should introduce you and mention the reason why you would like to be part of the company’s growth story and how you could contribute. If you are a hardcore techie not too comfortable with the syntax, etc., get help.
This is an absolute must-do when you apply directly to companies.
I. Follow these Resume Do’s and Don’ts
Here are some resume to-do and resume misfires:
- Tailor your resume to the job role you are applying for. A resume and a cover letter customized to the company requirement and job role always stand out from generic applications.
- Include your phone number and other contact information, in your cover letter, even if this information is already included in your resume.
- Include exact dates of employment. Otherwise, it may be a cause for suspicion on the hirer’s part.
- Do not use an email address that makes your hirer raise his eyebrows. Email addresses like firstname.lastname@example.org, email@example.com or firstname.lastname@example.org are examples of suggestive and silly email addresses. They do not give off a professional aura. Rather, use email addresses that preferably have your name as a way to identify you in the inbox, and do not have numbers that include your year of birth.
- Do not apply for a job role just because you want to complete your target of weekly applications! Make sure that you tick all the boxes required by the hirer, otherwise you are most likely to fall victim to “credential creep” and the hirer unlikely to ever look at the application and maybe blacklist you from further scrutiny.
- Do not include your photo in the resume, even if it is a professional-looking picture.
If you want to land your dream job, you have to aim for the “smart” way of doing things, not the median. The job market is filled with mediocre or junior talent, where an ambitious data scientist like you would want to make your mark. So do the extraordinary if you want your job application to come to fruition.
Go ahead and nail it!