How to Optimize Your CV for a Data Scientist Career
As the number of data science positions continues to grow dramatically, so does the number of data scientists in the marketplace. Follow these expert tips and examples to help make your resume and job applications stand out in an increasingly competitive field.
By Dorian Martin, Technology writer.
Looking to make your data scientist resume more attractive to employers?
You’re in the right place.
In this post, you’ll find expert tips, examples, and takeaways to make a resume that’s good enough for the job you really want.
What are Data Science Employers Looking for in a Resume?
Let’s begin by becoming a data science recruiter for a moment.
To stand out from the pile of other resumes and catch the attention of employers, we need to know what they’re looking for in candidates.
According to 9 Must-Have Skills You Need to Become a Data Scientist, these are the most important skills that attract employers:
- Education (at least a Bachelor’s degree in Computer Science, Social Sciences, and Statistics)
- R programming
- Python Coding
- Hadoop platform
- SQL database/coding
- Apache Spark
- Machine Learning and Artificial Intelligence
- Data Visualization
- Unstructured data.
So, as someone interested in hiring a data scientist, you’ll be more likely to look for these skills in the resume.
This brings us right to the first tip…
1. Put In-Demand Skills First
To save the hiring manager the trouble of looking for your skills, put them at the very top of your resume. Be sure to list them in a separate section to catch attention.
Don’t use a one-column bullet list for skills. It might take up a lot of resume space, so go for something different. If you’re not using any templates where the designer took care of space saving, you can simply list them in one sentence.
Another, more creative way is to differentiate each skill visually, like Upwork, a freelance platform, does in its user profiles.
In which order should you list the skills? The best bet is to have those in-demand skills first and move on to “least impressive,” if you will. Also, don’t forget to provide your evaluation of the skill level, e.g., “beginner, “intermediate,” or “expert.”
Takeaway: List your skills close to the top. They will help to draw the attention of the hiring manager and let them know if you’re the right candidate for the job.
2. Give a Detailed and Concise Overview of Your Achievements
This is another critical section that deserves being in the top half of your resume. It is the “Show, don’t tell” part that hiring managers want. Before asking you to disclose your flaws during the interview, they need to be drawn in with strengths.
Your main goal is to find and list your main professional achievements.
Here’s how to do it right:
- Use only relevant achievements. An achievement like “Providing expert dissertation help to students to improve their academic writing skills” sounds impressive, but stick to data science-related accomplishments
- Include numbers. A quantifiable accomplishment demonstrates your impact much better, e.g., “Analyzed marketing processes with custom machine learning algorithms to generate $2,000 in savings” is more impressive with numbers
- Use only one sentence per each achievement. Your resume should fit in one page, so be careful and limit each achievement to one sentence.
Pro tip: Use the “Skill – Activity – Result” formula for each achievement.
Here are these parts in “Analyzed marketing processes with custom machine learning algorithms to generate $2,000 in savings” example:
- Skill: using custom machine learning algorithms
- Activity: marketing process analysis
- Result: $2,000 in savings.
By structuring the achievements this way in your resume, you’ll make them to-the-point and clear.
3. Explain, Don’t List, Your Non-Technical Skills
Many data scientists make a mistake by presenting their soft skills in a bullet point list. While it’s important to mention them, don’t just leave it like that.
I mean, what does something like “verbal communication” really say about you to the person reading your resume?
That needs some details to demonstrate how good you are, right?
Here are some examples:
|Non-Technical Skill||Great Description|
|Verbal communication||Managed weekly meetings with clients to update them on projects and collect feedback|
|Written communication & teamwork||Worked with a team of 10+ marketing team, applying data analysis models to identify patterns in customer data|
|Storytelling||Created data visualization presentations and presented them to customers to explain the results of data analysis|
|Adaptability||Worked simultaneously on machine learning and data visualization projects to help identify unexpected problems & convert results into visuals|
Now, that sounds more interesting, right?
The hiring manager reading your resume will also agree.
Communication and teamwork are among the most important soft skills to present this way.
Why? Because the majority of data science professionals – about 60 percent – work in teams of five or more people, says the State of Data Science 2019 report.
Source: State of Data Science 2019
The only thing to remember when describing these non-technical skills is to write only one sentence for each one (just like with the achievements).
4. Match Keywords in the Job Description
It’s not a secret that hiring managers use keywords in job descriptions. In the data science industry, they include the names of skills, qualifications, certifications, and technologies.
Let’s take a look at this data scientist job posting from LinkedIn. It’s kinda long, but bear with me, it’s what you’re going to be doing soon, too.
The posting has most of the keywords underlined for you to find quickly.
See how many of them are there?
This job posting is literally packed with the keywords that the perfect candidate should have on their resume (that means you, too, if you’re applying).
If you’d like to apply for a position like this, it’s super important to have these in your resume: machine learning, written & verbal communication skills, software engineering, fast prototyping.
These are the primary ones if you will, but the more keywords you can hit with your real expertise, the better.
Important! Don’t stuff your resume with keywords. It’ll sound unnatural and suspicious for the recruiter. Do it naturally to avoid looking like you’re trying too hard.
Besides, keyword stuffing might also suggest that the candidate did it just to get to the interview, which in turn means they might not be qualified.
Your Next Steps
Follow the tips you’ve just read, create that awesome resume, and send it out!
Now, it should be much more engaging, detailed, and attractive to potential employers.
If you’re interested in knowing how companies hire data scientists, check out the guide below (it has a question bank with questions that employers use to assess the skills of data scientists!)
Bio: Dorian Martin has seven years of technical content writing experience and has helped numerous tech blogs create content marketing strategies. Currently, he’s working as a chief editor at Essaysupply.com where he helps with making quality content. Dorian’s biggest passions, apart from writing, are playing volleyball and watching music documentaries.
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