With the evolvement of AI and technology, data science comes at the forefront in almost every segment. Whether you are a fresh graduate or a working professional looking to switch to the field of data science or simply looking to add more strength to your resume, portraying effective data science skills can bring a significant change. Here is a step-by-step guide on how to create a resume that showcases your data science skills and increase your chances of getting your dream job.
Steps to Build Your Resume with Data Science Skills
Utilizing an online resume builder and any selection of one template from a variety of pre-existing resume templates can save you a lot of time. You could pick, for example, a suitable free resume template from the various collections in Google Docs or MS Word. These templates should be clear, visual friendly, visually attractive, well formatted, and laid out in a manner that you can fit tons of information into a page without creating a cramped and overwhelming feel.
This is a very critical step in your job-search application. To increase the chances of your resume being noticed, you need to tune it based on the jobs you are applying for. A hiring manager is typically bombarded with hundreds of resumes daily and thus will probably have few seconds to scroll through each of them before deciding whether they are worth a more detailed reading or not. Hence it is important that your resume catches the eye.
Here, the best approach is to read the description of the job and come out with the whole "wish list" and requirements for which type of candidate the company is seeking. The wanted skills, based on these facts, should then be incorporated with increased emphasis in the resume. Anywhere possible, use the keywords of this job description. This will help your resume pass through applicant tracking systems and land before human eyes for possible further consideration.
Now, knowing what exactly to include in your data scientist resume, rework and adapt this information to be laconic, precise, relevant, and well-presented. Exclude any information that adds no further value to this role and which wasn't mentioned or implicated in the job description. Keep your resume concise, purposeful, and full of good information. The bullet statements are brief and must not run over several lines.
The most common and preferable format of the sections connected with education and work experience is in reverse-chronological order. The most recent goes to the top, followed by the second to the last, etc. This will help a hiring manager easily get a view of your professional growth and the latest achievements.
This is a section that interests most recruiters, so it should be your major focus when writing a data scientist resume. List your jobs in reverse chronological order or include only the latest in case you have a very long work experience. If you lack real experience, consider including data science internships. For each of the jobs mentioned above, provide:
a. Dates of employment (Month and year for start and finish, or Present)
b. Your job title
c. Company name
d. What you achieved in that job
Try not to have any big gaps (more than six months) in your resume, especially in recent years. Even if all your previous jobs are not from the field of Data Science it is best to include them, but there's no need to get into too much detail. In case all your previous experience comes from an absolutely different sphere, try to understand what skills you actually practised there, which might be useful for data science, and what value you brought to the business. If you're a fresh graduate with no work experience or even an internship, you can simply skip this section.
Let's, however, go back to an "ideal" case when you have some experience in data science or data analytics. For relevant past roles, briefly describe in bullets what data-driven achievements you made and what value you brought to the business at each position. One of the first bullets has to be very impactful to convince the recruiter to keep reading. Many people make a huge mistake: they list duties and technical issues but do not highlight business-oriented achievements from previous job positions.
It is not all about the numbers and modeling; it's more how well one can communicate the insights with a view to helping the team and shareholders enable the company to make strategic data-driven decisions.
Instead of simply stating in your resume that you are a good team worker with proven communication skills, describe the jobs where you worked, in a probably multinational and definitely multidisciplinary environment with your colleagues, other departments, and data consumers. Focus on your part in these projects and the measurable contribution you provided.
This section is very important for junior and entry-level data scientists who most of the time have very limited or no work experience. Here the logic is very simple: the more work experience you have, the less real estate your Projects section should occupy on your resume, up to completely omitting it in case you are a senior data professional.
Whether you studied data science as an undergraduate, during your master's, or in a bootcamp, most probably you have already done one or more projects in data science or data analytics.
You may include courseworks, guided and capstone projects from bootcamp, freelancing works, contributions to GitHub open-source projects, and individual projects you completed on a topic of your choice. Unless you have projects to include, you really should do a mock one. After all, being a data scientist means being curious about data and the things you can learn from it.
You can mention relevant courses and bootcamps here in case you're applying for an entry-level role. Apart from Data Science and Data Analysis certifications, be sure to add the courses covering the following areas: programming, linear algebra, probability, or statistics. If you have an official data science certification, mention it here and also consider including it in the headline of your resume next to your title. For example, IBM Certified, Data Scientist.
Data science is one area that really stays at the top of all research and applications in this fast-moving world of AI and technology. Be it a fresher who wants to break into the industry, a professional looking for a career change, or someone interested in adding some extra points to their resume, articulation of data science skills can be a big deal.
Using a strategic approach to resume building from the choice of format to tailoring job description and highlighting both technical and soft skills, chances of landing that dream job can massively increase. Not only does a well-written resume represent your qualifications, but it's also representative of problem-solving skills, collaboration abilities, and contribution to the making of data-driven decisions. Properly equipped and with a number of strategies in place, a resume can become one of the most powerful allies on your data science career journey.