Data Science Manager Resume
Data Science Manager Resume – Writing resumes for data science job applications is rarely a pleasant task, but it is a necessary evil. Most companies require a resume to apply for any of their open jobs, and the resume is often the first step in the process to get past the “Gatekeeper” – the recruiter or hiring manager.
It seems like a simple task, but many struggle with it. Here are some tips on how to write a clear and compelling resume that will catch the eye of a recruiter/hiring manager.
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(This article is part of an in-depth Data Science Career Guide. To read other articles, please visit
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The first thing you should try in writing a resume is to keep it brief. A good resume should only be one page long, unless you have ten years
Then again, there are recruiters who will accept a resume longer than one page. Recruiters/hiring managers receive LOT resumes daily, and
“Let me be honest,” says Stephen Yu, president and chief consultant at data analytics consulting firm Willow Data Strategy. “The time I spend [on each resume] is less than 30 seconds before I meet with someone. If that resume doesn’t speak to me, which is only one out of ten resumes, I won’t even call the candidate. “
So, even though you may have dozens of data science projects you want to highlight, you need to prioritize. You want to condense your experience to the most important, relevant points, so it’s easy to edit.
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Create a data scientist resume and send it to every data job you apply for, but it’s also a good idea to try and add custom tweaks to your resume for each application you submit.
Although it requires more work than up front, adding small details here and there based on the job description will definitely impress the hiring manager/recipient.
This doesn’t mean you need to do a complete redesign and redesign every time you apply for a job! But, at the very least, if you focus on the keywords and important skills mentioned in the job posting, make sure the resume you send highlights your skills in those areas, and uses those keywords.
You may also want to look at the company’s website to try to get an idea of their preferred style and tone, and adjust the writing and aesthetics of your resume accordingly.
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“You have to find a way to position yourself so that when an employer looks at your resume, they go, ‘This person was just heaven-sent for my particular position,'” says SharpestMinds co-founder Edouard Harris.
(Obvious, but still worth pointing out: don’t list any skills or experience you don’t actually have. It’s okay to reframe your actual skills and experiences to fit the context of the job posting, but it is
While every resume will always include information like previous work experience, skills, contact information, etc., you should have a resume that is unique to you. It starts with the visual look of the resume, and there are many different ways to create a unique design.
You can create your own resume from scratch, but it might be easier to start with creative resume templates from free sites like Creddle, VisualCV, CVMKR, Enhancv, or even a Google Doc resume template.
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The resume template you choose is also important. If you’re targeting companies with a traditional feel (the Dells, HPs, and IBMs of the world), try to aim for a more classic, understated design style.
If you’re aiming for a company with a more startup element (Google, Facebook, Pinterest, etc.), you can choose a template or create a resume that’s a little more polished, perhaps with some unique graphics and colors.
You can also choose between a column layout (usually better for people who struggle to fit everything on one page) or a block-style layout where everything is packed into one column.
Either way, keep it simple. Again, a hiring manager may only take 30 seconds to review this document and make a decision, so when in doubt, keep things short and sweet. Don’t be afraid of white space in your resume design.
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Just remember that even if you start with a template, you should take the time to make it your own.
Once you’ve re-selected a template or decided to recreate one, take a second to double-check the contact information section. Your name, title and contact information should always be at the top of the page.
Some templates will have contact information at the bottom of the page. If so you will want to reset the order manually. If a recruiter or hiring manager decides to contact you based on your resume, you don’t want them to scroll through the entire resume before they see that information!
Key things to remember about your contact information and what you should keep in mind in the context of a data science audit:
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“Most of the people we interview have their GitHub pages listed on their resume,” says CiBo Technologies director of talent acquisition Jamieson Vasquez. “I think that’s important.”
These links should be clickable in all PDF versions of your resume, so recruiters can browse your profile directly rather than copy and paste. And, needless to say, when a recruiter clicks on your GitHub, they should see an active account with data science projects!
Your name, title and contact information should be immediately followed by your Projects/Publications section. In any resume, especially in the technology industry, the main thing you want to highlight is that you have created.
In the context of a data science resume (or data analyst resume), this may include data analysis projects, machine learning projects, and even published scientific articles or coding tutorials. Hiring companies want to see what you can really see
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“Data scientists have one goal and that’s to solve business problems. It’s not about how difficult the technical challenge is, it’s not about how good the solution is, or the tools you use.
So, while you can certainly include personal projects on a resume, you should choose ones that have some relevance or connection to the job you’re applying for. Projects should demonstrate your technical skills, but ideally, they will also demonstrate how your technical skills are applied to solving real business problems.
You definitely want at least one project or publication on your resume, but if you have room for more, add as many as you can fit in nicely. If you need help gathering projects for your resume and portfolio, we have a whole series of blog posts to guide you in building big data science projects, and another part of this guide discusses what projects you should include in an application. show at work and how you should show them.
When describing each project, be as specific as possible about the skills, tools and techniques you used, how you created the project, and what you used.
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Don’t worry if it seems like you’re repeating the same skills you plan to list in your skills section. In fact, the more often you can add those tools, technologies and key skills to your resume, the better.
Recruiters and hiring managers often use simple keyword searches to search for resumes, and you want your relevant skills to be highlighted in as many areas as possible when they’re looking at your resume.
A data scientist’s job isn’t just to crunch numbers, it’s to analyze data and then communicate those findings in a way that solves business problems.
Data science recruiters are looking for people who not only have the technical skills they need, but also people who are effective communicators and who understand the big picture. They want data scientists who can effectively tell stories with data.
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One way you can demonstrate these qualities is to highlight collaborative projects (which proves that you can work and communicate with a team) and map your achievements in the context of business metrics (which proves that you understand how your analysis applies to the larger business problems you’re trying to solve).
Of course, another great way to showcase your communication skills on your resume is through your writing itself. Make sure the writing is free of errors, and work to make sure everything is written simply and clearly. Having a fellow editor proofread your resume is always a good idea, but apps like Hemingway can also help you clean up and streamline your writing.
However, there is a lot of trust in automatic grammar suggestions. Even the best applications make more mistakes than a human editor).
Examples of this could be working with videos, posts, blogs, customer reviews, and audio. The experience of working with unstructured data is awesome because it shows that you can do incredible work with complex data, rather than just crunching numbers in the background datasets.
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Wherever possible, you also want to make sure you show the measurable results that your projects have produced. For example, if you created a machine learning model that will improve sales targeting by 15% as one of your projects, say so! “To travel
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