As per Glassdoor, ‘Data Scientist’ is at the top of the list of the best jobs in 2019. It pays well and also offers a very challenging and rewarding career path. As such, the number of data science positions have increased and so have the number of applicants.
Even if you ignore the competition, you still need to prove that you have the skills to be a part of the company. So, what is the first step to bagging the data science position of your dreams? A stellar and well-crafted resume.
Even before you meet the hiring manager, they will have formed an opinion about you through your resume. So, it better be attention-grabbing and lead them to call you for an interview. Let’s learn how to do this.
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Most candidates make the big mistake of preparing one resume and sending it off all potential employers (and oftentimes mistakenly cc-ing them all). This is a very unfruitful practice; it won’t get you the results you want. So, if a company puts out an ad for a data scientist whose primary skill is Python and you send them a resume explaining how you are King of R, then sorry; it’s not going to work.
Each of your resumes should be tailored to the position and vacancy you are applying for. The same resume can be sent out to a few different employers, but even then minor tweaks will have to be made. Also, keep in mind the following pointers as you begin making your data science resume:
- Keep the resume one page long. Until and unless you have 15+ relevant experience in the field, do not go over one page.
- Use whitespace generously.
- Use headings and subheadings where appropriate. It makes the resume more readable. So does highlighting.
- Use legible fonts. Most candidates in an attempt to be fancy, use cursive fonts (like Lobster). Or they take it to the other extreme and use casual ones (like Caveat). Avoid these extremes. Keep it functional and professional. Use fonts like Arial, Times New Roman, and Proxima Nova.
- Don’t overdo the colors.
- Proofread and grammar-check your resume always. Run it through Grammarly or have a friend look at it. Even one spelling mistake can ruin your impression.
Sections to include in your data science resume
Here are the basic sections to be included. You can add and omit as you wish, but these encapsulate the basic details that a hiring manager would need to know. The order can also be as you wish.
- Resume objective/ summary
- Work experience
- Key/ core skills
- Education and certifications (if any)
- Any projects or publications
- Basic info about you
- Hobbies section (or one that shows your personality like ‘most proud of’)
What to include in each section
Resume objective/ summary
This is the first section that the recruiter’s eyes will fall upon. It is a very crucial section since it will help you to get your foot in the door and compel the recruiter to read the rest of your resume where you expound upon your achievements.
So, which one do you write? Objective or summary?
If you are a recent graduate or a fresher in this field, then you write a resume objective. If you have relevant experience and results in the field, then you write a summary.
Here’s how to write a resume objective
Recent graduate from XYZ University with a Bachelors’s in Computer Science. Applied my analytical and strategic skills in building projects that won me the Global Data Science Challenge in 2018. Eager to apply my skills to solve real-world problems now.
Interesting. You’d want to read further, no?
Here’s when you would not want to read further
Recent graduate from XYZ University with a Bachelors’s in Computing and IT. Looking to learn data science technologies and become skilled at them.
Whoops. That one gets tossed in the bin. Mention your skills, any achievements if you have them, and what you can do for the employer instead of the other way around. Next, here’s how to write a resume summary:
Ambitious data science engineer with 5+ years of experience. Specializing in using Tableau to create clarity-generating data models that distill large amounts of data into easily understood visualizations. Winner of the Annual Tableau Challenge.
Here’s how to not write it
Data science engineer with extensive experience can do statistical analysis, data cleaning, data visualization and also lead teams.
Conclusion: avoid vague claims. Include hard facts and numbers to make your expertise more tangible.
Mention your work experience in reverse chronological order. This will allow you to begin with the most impressive points since your responsibilities and results would have scaled up since your career began. Next, pick your best projects to include. No need to mention every project you’ve worked on under the sun.
Finally and most importantly, aim for impact. Every data science resume will mention statistical analysis, data visualization, and data mining. But the impact that you would’ve created would be unique to you. So include hard facts and numbers about how your efforts and skills helped the company to grow.
Here’s a possible format
Position and company name
Worked from ____-____
<Here you talk about the impact you have created through your responsibilities and any significant awards that you might have won>
Here’s an example to make it clearer:
Data scientist at Goldman Sachs
Jan 2015- October 2019
- Created and implemented models for predicting loan profitability. Achieved a 20% improvement rate in the quality of loans approved.
- Led a data visualization team of 20 to improve the quality of statistical reporting.
- Won the Global GS Data Science Competition 3 quarters in a row.
Again, avoid vagueness. Support your claims with facts and figures.
Key/ core skills : If the structure of your resume allows it, divide your skills into hard skills and soft skills.
Hard skills in data science include : Python, R, SQL, APIs, Data Cleaning, Data Manipulation, Command Line, etc.
Soft skills include : leadership, analytical thinking, strategic thinking, creativity, teamwork, etc.
Education and certifications
Most people include this section before the work experience section. But, the latter is more relevant to the hiring process, especially if you have been in the industry for at least 2 years. So, place it accordingly.
If you’ve passed university, then there’s no need to include your schooling. Also, follow a reverse chronological order wherein you mention your most recent degree first. Mention any interesting projects or awards you won during your program or any mathematical/ computing clubs/ societies you were a part of.
If you have any certifications, include those as well. For example, when you are applying for a data science related job, a certification of data science from a reputed institution would help you get the interview call.
This includes your name, city, state (and country if you are applying for an overseas job). Also, include your active email address, telephone, link to your LinkedIn profile, and blog link if you have one. Since you are applying for a data science position, recruiters will want to see which projects you have worked on or are currently working on. So, include a GitHub link as well.
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These will help guide you in making your data science resume. It is as important as any other aspect of the hiring process. So, make sure to give it your best by following the above tips and guidelines. We’ll see you on the other side of being hired!
Is it worth being a data scientist in 2022?
Data Science is indeed trending the charts with our ever-increasing dependencies on data and technology. There is a huge gap between the demand and the supply of data scientists which makes it one of the highest paying fields of 2022.
A data scientist with 5 years of experience earns around $300,000 per year. A decent data scientist earns around $123,000 per annum whereas the median salary of data scientists is around $91,000 per annum. This is just the base salary. Data scientists also get an attractive media bonus of around $8k within a range of $1K-$17k
What skills are required to be a data scientist?
The following skills are necessary to be in your arsenal if you are a data science aspirant and want to become crack good opportunities:
1. Statistics and Probability
Statistics and Probability are the two most important mathematical concepts of Data Science. Descriptive statistics like mean, median, and mode, linear regression, hypothesis testing is some of the topics of statistics and probability.
2. Programming Language
You must go with one programming language and master it to code in it. There are plenty of languages out there but Python is the most preferable language due to the libraries and modules it provides.
3. Machine Learning and Deep Learning
Machine Learning and Deep Learning are two separate domains and the subsets of Data Science at the same time. These topics will help you to get afar in data science.
4. Data Visualization
Data Visualization is the art of visualizing the data in the form of charts and graphs to make it more understandable and profitable.
What are the applications of data science?
Data Science is governing a lot of technical domains as data has become a necessity. The following are the major applications of data science:
1. The finance and banking sector is one of the earliest sectors which started using data science, as there is a dealing of a huge chunk of data on a regular basis.
2. The healthcare sector uses data science predominantly in areas including Image diagnosis, research in medicine, and genetics.
3. Other fields include airlines, transport, gaming, and manufacturing.