10 Data Science Resume Tips that Will Land You a Job
Is data science still in demand in 2020? How do you write a data science resume? These are some basic questions asked by many people who are interested in data science and willing to get a job in that field. Data science-related jobs are quite famous and demanding in many countries. As an example, in the US, there are over 2.3 million data science jobs by 2020. So, the first step that you need to accomplish in order to land your preferred data science job position is creating a great resume. In this tutorial, we are going to discuss the 10 most important data science resume tips which will help you to have more interviews.
Why is a good resume important for a data science job?
Whether you will get an opportunity to face an interview will mostly depend on your resume. The main purpose of having a data science CV is to market your skills and get the job you preferred most. To do it successfully, you need to leave a good impression on potential employers through your data science resume. You have to prove to employers that you are one of the best fits for the specific job position.
To stand out among all the other candidates, you need to demonstrate your capabilities quickly. Usually, eight out of ten CVs are discarded just after 10 seconds of quick glances by recruiters. Therefore, you can understand that a good resume is a vital aspect in the process of getting a data science job.
Basic mistakes you make when writing resumes
Reviewing CVs is basically a screening process. Avoiding general mistakes that people do when creating data science resumes, definitely increases the probability of winning this screening process. So, let’s briefly discuss what are the mistakes that you should avoid when creating a data science skill resume.
- One of the most common mistakes candidates make is sending generic CVs. It should be avoided as most of the time recruiters hire for specific job positions that have specific requirements. Thus, you need to focus on the job skills that are relevant to the job role.
- The next mistake is creating CVs which don’t have any uniqueness or personality. This can be eliminated by adding some projects or details unique to data science in your CV. It makes the one who reviews CVs remember that you are in a positive manner which creates a high chance of getting recruited for the position.
- Adding a verbose summary or an objective is another thing that you should avoid in your data science resume. The summary is the first thing that hiring managers would notice. Therefore, it should be quite short and simple. You can describe your educational or data science experience in the relevant sections instead of mentioning them in the summary.
- Another significant mistake is omitting relevant certifications and skills. Besides, not adding the links which indicate your digital presence is another big mistake most candidates do. It is important to include links to your relevant digital accounts such as Github and LinkedIn.
Data science internship resume vs Experienced data science resume
If you are a fresher, creating a data scientist entry level resume can be a tough task for you. When it comes to applying for an internship position, there are some important facts for creating a resume which you should be aware of.
If you apply for an internship, you might be a newbie to the data science industry. Suppose that you are creating an entry level data scientist resume; no matter how talented you are at data science technologies such as Tensorflow and Python, you may miss the job if you won’t pay good attention to your resume. As a newbie, you may not have a big experience in the industry. In that case, as you can include the experience you obtained in your data science class projects and other learning projects.
However, if you are creating a CV for data scientist position which requires good industry experience, don’t forget to emphasize your working experience there. It is the most crucial fact that provides you with a high chance of having an interview. Moreover, If you are a well-experienced person in the industry, you can include and highlight the facts such as years of experience.
The majority of the companies use automated software to review resumes and filter them using specific keywords that are related to the job role before sending them to hiring managers. So your resume should include all the details of your educational qualifications and data science-related experience regardless of the position that you are applying for.
10 best tips for a good Data science resume
Now we are going to discuss the best tips that are useful in creating a data science resume. Read, understand and apply the below tips in order to land your data science job.
1. Write a suitable career objective
Make sure to write a great objective statement for your CV by studying the description and requirements of the job. You can find the duties and responsibilities related to the job position in the job description provided by the employer. The job requirements section gives you a good overview of the qualities, qualifications, and experience required for the specified job.
By reading the job description and requirements, you can have a good idea of what the employer expects from the candidates. In the career objective section, you should emphasize that you have similar interests expected by the employer.
2. Pick a suitable Resume Template
Selecting a suitable data science resume template is another essential fact in creating an attractive CV. All the resumes usually include information such as related qualifications and data science experience.
It’s important to have something unique in your resume to makes it more noticeable. The most common format for resumes is reverse-chronological which means your latest work experience should come at the top of the resume. This format allows the recruiter to immediately grasp the value of you. Other than that, there are some other resume templates such as Functional Resume and Combination Resume.
If you have strong skills, but a weak work history, then the Functional resume format is recommended for your data science internship resume. Combination resumes combine both the “Functional” and “Reverse-Chronological” formats and they are more suitable for candidates who possess previous working experience.
3. Get the basics including accurate contact details
It is not required to be creative when you add your basic details such as your name, age, and contact details. The only requirement here is that they should be 100% accurate.
Check multiple times to make sure that the phone number and the email you added is accurate. Also, don’t forget to add the relevant links which show your digital presence such as the GitHub link and LinkedIn profile link.
When adding the LinkedIn link, you don’t need to copy and paste the whole profile URL as it can be quite clunky. LinkedIn facilitates personalizing the LinkedIn profile URL as you want under some rules. You can check the steps on how to do it here. You can also add some URL shortening methods such as bit.ly .
4. Understand employers’ requirements
No matter how talented you are in data science, it will be impossible to have an interview if you don’t understand the employer’s requirements well. This fact is valid not only when writing an experienced data scientist CV but also when writing a data science internship resume.
You need to carefully go through the job descriptions and the requirements which are provided by the employers. Then only you will be able to understand their requirements well. When making your resume, make sure to be specific according to the employer’s requirements.
As an example, you may be an expert in the R language. However, in the job description, if they have specifically mentioned that Python knowledge is a must, then don’t try to emphasize your R language knowledge more than Python. You need to balance it and properly add it to the CV.
5. Highlight relevant data science experience and past Projects
The projects and publications section can be considered as the most important section in a resume. Especially in the technology industry, the main thing you should highlight is your portfolio. You don’t need to add all the projects you have worked on so far. Select some of the best data science projects for resume which can perfectly demonstrate your data science knowledge.
When you are describing each project, be specific as possible about the skills, tools, and technologies you used. Make sure that you have also mentioned the libraries and languages you used.
6. Write Technical Jargon and include numbers effectively
Since the data science-related professions are highly technical, you may need to add some technical words and algorithms to your resume. However, since the hiring managers may not understand those terms, use plain English as much as possible.
It’s better to use numbers and amounts such as fractions or percentages in order to make your resume easy to understand. By using numbers, you can impress the recruiters as well as drag their attention to your resume.
7. Include a pro-education section where you can prove your data science knowledge
This section can be considered as one of the most important sections in the resume. The majority of the recruiters pay special attention to the educational qualifications of the candidate. Therefore, you need to highlight some important facts such as the type of the degree or other educational qualifications and the date of graduation within this section.
Mainly highlight the educational qualifications that are related to the required skills mentioned in the job requirement section. If you are not graduated yet, don’t skip that section and simply add the university or the institute name and mention the expected graduation date. In case you have multiple advanced degrees, you need to rank them with the highest degrees first.
8. Write about your Hard & Soft Skills
One of the common mistakes which candidates do in resume writing is not giving value to their soft skills. Sometimes they don’t bother to add them to their CVs. But soft skills are quite important when it comes to writing a data science skills resume. Hard skills are always easy to measure as well as to recruit. But soft skills are difficult to measure and recognize. So mentioning your soft skills adds extra value to your resume.
The work experience, educational qualifications, and specific skills in the data science field can be considered as hard skills. Creativeness, communication, and leadership talents are some of the most important soft skills that you should improve in yourself as well as add in the resume as a candidate for a data science job.
9. Using proper keywords for the resume
Most of the recruiters use the Applicant Tracking System (ATS) in their recruitment process. ATS scans and parses the resumes based on the presence and frequency of relevant keywords in them. Then only the winning resumes are handed over to the recruiters to review manually.
Therefore, make sure to add relevant keywords in the resume at an acceptable frequency according to the job description and requirements. Rezrunner is one of the tools that you can compare your resume with the job description and check whether ATS will parse your resume.
10. Highlight data science-related Certifications, Awards, & Honors
Highlighting data science-related achievements is an ideal way to grab an employer’s attention to your resume. Either it is a data science intern resume or a resume data scientist who is well experienced, emphasizing achievements such as certifications, awards and honors will increase the potential of getting an interview for the applied position.
Even if you don’t have long term data science experience, you can prove your skills and talents by highlighting these kinds of achievements in your resume.
In this article, we discussed some of the best tips you can apply when it comes to writing a data science resume. Writing a resume that is good enough to get a job interview is not an easy task. The details and facts mentioned above will be quite important and useful for you in creating a data science resume not only to grab data science internships, but also more experienced data science jobs.
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