- How can you build an effective and powerful data science resume that will win over recruiters? Here are 4 key aspects you should be aware of
- We have parsed through thousands of data science resumes and spoken to multiple recruiters to understand what it takes to craft the ideal resume
- This is part of the ‘Ace Data Science Interviews‘ course, where we guide you on how to land and clear your next interview
Are you applying to data science jobs but not receiving any phone calls for the position you want? This is among the biggest gripes aspiring data science professionals have. Regardless of the role you’re applying for (data scientist, data engineer, data analyst, etc.) – clearing that first hurdle is a significant obstacle.
If you find yourself in a similar position, there’s a good chance recruiters are passing over your resume. It is the single most important aspect of landing a data science interview. A poorly crafted resume or too many irrelevant details will land your resume in the rejection pile.
Here’s the good news – crafting the perfect data science resume is a skill you can learn! Once you know how to expertly update your resume, you’ll be able to effectively market your skills when applying for your next data science job.
Now, I will take you through a step-by-step process to build an awesome data science resume. If you follow this process sincerely, there’s a good chance your resume would leave a good impression on potential recruiters.
If you’re struggling to land or clear data science interviews, we have curated the perfect course for you. The ‘Ace Data Science Interviews‘ course is an amalgamation of our combined experience of taking hundreds of interviews to help you land your dream data science role.
Table of Contents
- Structure of your Data Science Resume
1.1 What is the right length of the resume?
1.2 Create Differentiated Areas
- Adding Content and Information to your Data Science Resume
2.1 Information Prioritisation
2.2 Make your Content Crisp and Clear
- Get Feedback from Industry Experts
- Build your Digital Presence
A good way to think about your resume is to look at it as a real estate
This is a very intuitive way of crafting your resume. Let me explain. In any house, you have a fixed area and a floor plan to work with. You need to make sure that things fit in neatly in whatever space that is available to you.
Similarly, your resume has limited space which you should use judiciously and tell your story effectively.
Keep this analogy in mind as we walk through the steps below.
1. Structure of your Data Science Resume
First, we should consider the overall structure of the resume. This will help us plan the different sections we should include and how lengthy (or short) those sections should be.
What is the right length of the resume?
While building a resume, one of the most common dilemmas is what should be the length of the resume? Ideally, a single page is sufficient. If you keep your resume crisp and to the point, it would ensure that the interviewer/recruiter is reading what you want them to read.
A one page resume is recommended, while a two page one is also acceptable. Anything beyond two pages increases the chances of getting your resume rejected. In my experience, crafting a multiple page resume is usually skimmed over by the recruiter – not an ideal situation to be in.
Have a look at the below resume. It stretches all the way up to 3 pages. This is highly undesirable and it would leave a bad impression on the recruiter or the interviewer:
Now, take a look at the below resume. It’s exactly what a data science recruiter would look for. It becomes really easy to skim through it and get an idea about your skills and capabilities. And given that recruiters are parsing through hundreds of resumes a week – this is what they keep an eye out for.
So, try to keep only the relevant information on your resume. For example, if you’re applying for an NLP role, there isn’t much need of mentioning how you took accounts as a subject back in college. Space is absolutely vital – use it wisely.
But what if all you’ve achieved and done is simply too much for a single page? Here’s my advice – do not hesitate to cut down the content and keep only those details that would be in-line with the job you are applying to.
Create Differentiated Areas
Once you have selected the information that you would like to display on your resume, it is time to identify the right sections or areas on the resume where you would put your experience and information.
Here are a few key points you should consider while preparing your data science resume:
- Make sure the contact details take as little space as possible and mention your current city instead of your entire address
- Your resume should also have an objective but it should not be more than 2 to 3 lines
- Other areas that you should not miss are in your resume are:
- Your work experience
- Awards and achievements
- You can also include a few optional sections like:
- Your performance in data science hackathons
- Contribution to open source projects
- Community involvements
- Hobbies and interests
- Things you can afford to leave out of your resume:
- Soft skills
- Your Photo
2. Adding Content and Information to the Resume
Here comes the meat of your resume – your experience and projects in data science. Again, your ability to fit everything into a single page will come in handy. But how can you do that? Let’s take a look!
Let’s pay attention to what you should put in each of the sections discussed above. This exercise is crucial because you want to tell as much about yourself as possible without including any irrelevant information. On top of that, due to space constraints on the resume, you might even have to sacrifice some of your relevant and important information.
Hence, prioritising what to display and what not to on the resume is a critical step. It depends not just on your knowledge and work experience but also on the nature of the job you wish to apply for.
For example, let’s say you have worked on a Natural Language Processing (NLP) problem and you go ahead and apply for an NLP Data Scientist role. However, most of the projects mentioned in your resume are related to basic machine learning challenges. This will be a very risky scenario for you as the recruiter might well reject your resume because he would not come to know that you can even handle NLP tasks.
I completely understand that it’s difficult to leave information out. But that’s the price we have to pay when we want to land our dream role. So, let’s see what kind of information we should include and what to exclude in different sections of the resume:
- In the Skills section, you should focus on your technical skills or the hard skills (instead of soft skills). Almost every job portal nowadays screens resumes based on hard skills or the keywords related to hard skills
- Similarly in the Experience section, look at the past projects you have done and select the projects or roles which are most relevant to the role you are applying for. If you need to add details around these projects, just explain them in a sentence or two or in bullet points. Note: Don’t add any project that does not involve data science or analytical problem solving. That adds almost no value from the recruiter’s perspective
- You should also add relevant certifications, your blogs (if any), academic achievements, or performance in data science competitions
Awesome! The basic template of your resume is ready. So, what’s next? Your resume should pack a punch so let’s see how to make it impactful.
Make your Content Crisp and Clear
To make your resume stand out from the rest of the candidates, incorporate the below points I’ve mentioned:
- Use active voice instead of passive voice. This helps keep the sentences shorter and easier to read. This would make the resume look much more action oriented
- You should quantify the uniqueness and benefits of what you have achieved in every project on your resume. For example, you can mention the impact of your projects on the business in terms of increase in revenue, reduction in cost, or return-on-investment. This point is important because this is something which you would do regularly as a data science professional. You will take ambiguous problems and convert them to data science problems – so use your resume to showcase your industry-ready mindset
3. Get Feedback from Industry Experts
Now that your resume is all done and ready, there is still one final step left for you to execute – getting practical and experienced feedback on your resume. This is important because when we work on something with full dedication and sincerity, we often tend to overlook its flaws and drawbacks. It is a human tendency. The only way to rectify this problem is by getting your work reviewed by the right people.
For example, at Analytics Vidhya, once I have written a blog article on a project, I get it cross-checked and reviewed by my teammates. This adds a fresh perspective to my thoughts and the feedback that I receive helps me a lot in further improving the article for the community.
Hence, to build a solid data science resume, it is imperative to get it reviewed by industry experts, data scientists, subject matter experts, etc. This is where your networking skills would be useful. Share your resume with the people in the industry and take their feedback.
Ask specific questions to these people. For example, you can ask which three out of five of your projects should be there on your resume. Or how you can quantify a certain task you performed in college or in a previous organization.
4. Build your Digital Presence
We have so far seen the essential ingredients to build a great data science resume. However, in today’s world, having a good resume alone might not be enough to land that coveted interview call. Especially if you are applying for the Data Scientist role.
Your resume should be supplemented with your digital profile as well.
We are living and thriving in the midst of a digital revolution. It stands to reason that the recruiting process would incorporate that as well, right?
Let me give you my example. I take quite a few data science interviews every week. Before I get on the call or enter the interview room, I always check two things:
- The candidates’s GitHub profile, and
- His/her LinkedIn profile
I additionally look at the projects mentioned on both these platforms. Are the projects relevant to the current role? This helps me visualize the candidate’s profile so I can structure my questions in a certain manner. I can also gauge whether the skills mentioned by the candidate in his/her resume are reflecting in their GitHub profile.
To build an impressive and powerful digital profile, you can take cues from the following ideas:
- You need to have a good presence on LinkedIn. Majority of the interviewers prefer looking at your LinkedIn profile before the interview (we spoke to multiple recruiters and can confirm this)
- Build a GitHub profile. Share your personal projects and the full code on this platform
- Maintain a blog on data science. Share your knowledge with the data science community. This helps build your own brand
- Regularly answer and solve data science related queries on platforms like Discuss, StackOverflow and Quora
This is not an exhaustive list. There could be other means and tools as well to enhance your digital presence. However, keep in mind that you are building this digital profile to make sure that when you go for your interviews, it should reflect your expertise.
It is also not feasible to maintain your profile at every major platform. Therefore, you have to be selective when it comes to shaping up your digital profile.
But let’s settle this now – LinkedIn and GitHub profiles are mandatory to have. Absolutely no question around that. Apart from these two, you can have a presence on blogs, podcasts, or YouTube as well (but these are good to have profile rather than mandatory ones).
This article should be a good starting point for your data science job application process. As I mentioned earlier, having a solid, relevant and impactful resume is mandatory if you want to land that dream data science job.
But this is one step in the entire data science interview process. There are multiple rounds you should be aware of (and be prepared for), such as:
- Telephonic interview
- The in-person interview (which usually has several rounds)
And so on. We’ve covered each of these steps in our comprehensive ‘Ace Data Science Interviews‘ course. The course has several handouts as well to supplement your learning including a comprehensive interview guide with over 240 questions. So start your interview preparation today!You can also read this article on our Mobile APP