This article was published as part of the Data Science Blogathon.
Job hunting can be the most frustrating time of your life. It is worse if it is cooler or if you are trying to enter a new field like Data and Analysis.
An average time period for a job search can take more than 5 months of endless requests with very few actual interviews.
The strategy of 6 steps I suggest to follow:
- Acquire the right skills and competencies
- A great resume
- Have a specific resume with keywords
- Planning a specific job search
- Using LinkedIn for career opportunities
- Leveraging community and connections
Let's dive in!
1. Appropriate skills and competencies
It goes without saying that acquiring the right skills is critical to landing the job you want.. You need to make a list and roadmap of the minimum skills required to get the data science job you want and then start working towards it..
But, How do you know what skills are required?
Good, the quickest and smartest way would be to simply go to the job portals and search for the position you want. Analyze the description of each position and see what skills most organizations require for the same position. Another way would be to simply browse and search the internet, either on Quora, medium blogs or LinkedIn.
Building the skill set
Once you know what roles you want to target and what skills to acquire, you will need to do a study plan and the amount of time required. If you are currently working, I would need to dedicate at least 1 hour daily during weekdays and a minimum of 10 hours during the weekend, which is an average of 15 hours per week.
Depending on the skills you need, you will have to opt for some courses or other forms online / offline to improve skills and undertake the necessary projects to show your potential.
For instance, if you are applying for a data scientist job, You will need to learn the basic skills first and then apply them by doing projects, participating in Kaggle competitions, DataPeaker Hackathons, etc. possibilities of receiving large calls. Just make sure you don't do the basic machine learning projects like Titanic, house price prediction, etc. Your resume must have 3 good and unique projects that you can talk about in detail.
So now you have the necessary skills and projects in your portfolio. The next step will be to create an amazing resume that will catch their calls and get recruiters interested in you.. Yes indeed, an impressive resume doesn't mean using fancy colors and templates.
Dos and Don'ts on a Resume
I have created my set of rules and checkpoints that I recommend when doing a resume for a job in the tech field. These are my opinions that I have obtained from the experience of looking at more than 100 resumes.
First, your resume should be on one page, unless you have more than 15 years of experience and a huge amount of experience and achievements.
Your resume must be sharp, concise and include only all relevant information. Add all your work history, projects and all the skills you have learned since birth would not make your resume great. Include only relevant information.
Choose a good template that clearly shows all work experience and skills. Note that the simpler, best.
Avoid including your photo.
Don't include fancy icons or rating systems to show your skill level. All of these things make it difficult for the ATS (Applicant tracking system) scan and analyze information.
Use action verbs when describing work experience and projects.
Make sure the points are short and include your task and the results obtained.
Place the sections that are relevant (as current work experience, personal projects) at the top and less relevant sections at the bottom (like your education). A bad example of a resume:
The resume in the image above uses too many quirky colors and icons that are simply not necessary. It not only makes it look bad, it also makes the ATS difficult to parse the containing text.
A good resume:
The above resume template ticks all the boxes and can be modified as per your needs. The simpler the resume, the better it will be. There is often a misconception to make your resume stand out, should make it elegant and eye-catching. That is false.
3. Targeted resume and keywords
Your resume should not include all the information and skills that you have and allow the recruiter to find what you need from him. It should only contain what the recruiter wants. Your resume should show a picture that you are the fit candidate for that job.
What does a targeted resume mean?
A specific resume means modifying your resume to better suit the job you are applying for.. For instance, if you are applying for a data scientist job that focuses on NLP skills, try showing and highlighting the NLP tasks you did in your current job. Alternatively, show more personal projects based on NLP and also include all NLP skills. In essence, recast your resume for work to make it a specific resume and not a generic resume where the recruiter has to find what they want.
Secondly, add the keywords mentioned in the job description. Job descriptions include many skills and keywords that they expect from the candidate. You may have most of them, but you may have used different words on your resume or omitted them.
For instance, a job description mentions: “Data cleansing, linear and nonlinear modeling, Sklearn, Pandas“And your resume has:”Data pre-processing, regression, classification, Python, scikit-learn”. Now there is a need to remove the skills you have with the skills / keywords mentioned in the JD. This is to maximize your chances of getting a high score on the ATS which rates resumes based on the number of keywords found on your resume and, Consequently, put it on the list. An RR.HH. you can only choose the first ones to check them manually and then preselect them. Resumes with very low ATS scores may be automatically rejected by the ATS itself. This is the reason why many applications get an automatic rejection shortly after the request.
Having said all this, it is not necessarily important to include all the keywords in the JD and change your resume for each job, but please modify it slightly if you think there must be any change.
4. Specific job search
A normal and inefficient way to apply for jobs would be to mindlessly submit your resume to every job posting you find on job portals and expect to receive callbacks.. While you may get some, but mostly you will get very poor response. This can be due to a number of reasons: the company is not active on those platforms or their resume is not good enough. But the main reason is the number of requests they receive. On average, a data science job posting receives 200 requests in 24 hours, where vacancies are no more than 5 O 10
Do you see the problem there? There is a lot of competition and your application needs to stand out and be visible to the hiring manager. The first step is to make a specific resume and the second is to contact the hiring manager or recruiter. The goal is to get their email id and send them your request directly / resume. This way, get direct access to your mailbox, while the resumes of all other applicants will still be in your database.
"Hello XYZ, I came across this job posting on ABC and I think it might be the right candidate for the position. I have N years of experience and my skills include JKL. It would be great if you could take a look at my resume and see if I fit. Thanks”
The first step in your referral-led job search is to make a list of potential companies you would like to work with and have the roles you want.. The next step is to connect with the people of those companies through LinkedIn.. Employees who work there can help you with referrals, what is the best way to get a job. The strategy is to connect with at least 5 people in a company and start the conversation with them about the company / job. After having a conversation, ask if they would be willing to refer you to the position at their company if they have openings.
However, this strategy may not work when 100% as you would expect because people may not accept your connection request or may not respond to your message. Or even if they do, they may not have vacancies in the company. Therefore, the best way to maximize your chances is to add more people, which will increase the probability of getting referrals. What's more, do some research on job postings and prepare yourself with the job ID you want to be referred for. Just by asking “Please, help me with a referral” you will get nothing. Have a professional template ready where you just change the job names and ID and send it to the person concerned.
"Hello there, ABC. I see you are working at XYZ as a data scientist. I really wanted to know your company and its role “.
Second message: “Excellent! I have been looking for similar roles for some time and was wondering if you would be willing to send me a reference. Job ID is 12345. Thanks! “
5. Use LinkedIn
LinkedIn is the best way to expand your network and, as a last resort, job opportunities. Although the LinkedIn job portal receives many job offers, I did not have a good experience with him. A better way to search for data science jobs on LinkedIn is to search for people who are hiring. Just go to the search window and search for words like “Data Scientist Hiring” (or the position you want). Connect with them and show your profile and resume.
Another way is to stay active on the platform and keep an eye out for people posting posts for data science vacancies and job updates.. There are many vacancies that are filled this way and those who respond first get the upper hand. The trick to getting more opportunities like this is to connect with more and more people and always be in sight..
Have conversations with other people in your domain and tell them about your job search. Ask them to send you the vacancies they find. This way, you take advantage of their connections and their active time on LinkedIn.
Finally, the most important strategy is to publish content on LinkedIn. Writing posts regularly will not only increase your connections and followers, it will also attract recruiters to your profile. Hence the need for an up-to-date LinkedIn profile.. Make sure you have a good profile picture, abstract, About, Projects, certifications, etc. You can post about encoding problems you solved in Leetcode / Hackerrank, post about your Kaggle projects, etc.
6. Community and connections
The community and the close connections it establishes are other great sources of opportunity.. Many vacancies are filled this way. Connect with people in your domain and have conversations with them. The more you talk, the better the relationship becomes. Request their contact numbers and stay in touch with them. This way, they will send you any job postings they find in WhatsApp groups / Telegram / Slack.
Talking about group chats, many communities have a group on these channels where many opportunities are posted every day. Active people who act quickly on them get the upper hand. Most of these data science jobs aren't even posted on job portals or LinkedIn. So make sure you are a part of these communities and maximize your chances of landing a great job..
For instance, some of the great communities / groups are ML con Harshit Alhuwalia, Co-learning room-artificial intelligence room, KaggleNoobsetc.
Job hunting in data science is a difficult task and must be planned separately. Consider it your highest priority task and dedicate 30 minutes daily to the points we discussed above. Having a period of time to get a job should also be on your mind. Following this strategy and optimizing your data science job search will surely bring results.. Remember to be patient and not burn out of frustration. It happens to the best of people and it will only grow from it. Just keep this growth mindset in yourself and work towards the goal until you achieve it!!
Connect with me on LinkedIn if you have any questions or want more information.