Preparing a good resume for a Data Scientist position or a networking event is never easy. There is a significant amount of learning, exploration, understanding and analysis that is needed to prepare the “champion” curriculum. What Do Recruiters Look for in a Data Scientist Curriculum? Follow the article to understand.
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As Data Science is an interdisciplinary field, there are many things that a Data Scientist must know; algorithms, scientific methods, processes, tools and systems to extract insights from the data and make decisions based on that knowledge.
Regardless of the amount of work you have dedicated to your Data Science journey and the Data Science certifications or courses you have taken, your selection process starts with a good resume. It's your calling card. Its main credential. A summary of your professional life. How is it possible that so many candidates do not pay the least attention to this? The recruiter will have the first contact with you through your resume and will often have a few seconds to decide whether your resume deserves to be checked a second time or if it goes to the trash can. You have a few seconds. Don't waste your chances.
We will list below the points that a recruiter expects to see on his resume as a Data Scientist . These points reflect the views of more than 80 recruiters and references are at the end of the article.
1. Customize Your Resume for Each Job
Employers always look for the "relevance" of your resume to the job description. They care not only about you wanting a career in Data Science, but about you wanting a career with them. Before starting to build your resume, check who you are sending the resume to and highlight your experience in the market in which the company is a part.
If the job description is, for example, a finance or food and beverage company, you can always try to show at least some knowledge of the domain.
Realistically, your resume won't be much different for each application you make, but it should be a little different.
One recruiter considers this: “We appreciate curricula that excel in the curriculum pool; we like candidates who want THIS job, not just ANY job. "
Sometimes candidates fail to understand the importance of changing resumes according to the job description, and this usually encapsulates one of the main reasons for rejections: domain knowledge. Recruiters assess whether you are suitable for the company, the department, the type of project you are working on. Does your resume reflect the fact that you fit in well with what the company needs?
2. Choose the Right Keywords
In addition to researching the necessary adjustment in the curriculum, it is important to choose the right words that demonstrate your potential adjustment to the recruiter. Many companies use candidate tracking systems to analyze resumes. The curriculum scanning software is designed to scan a curriculum for professional experience, skills, education and other relevant information.
Make sure the keywords on your resume and your experience are formatted to reflect the company's mission and vision (you are trying to be an ideal candidate).
The company's curriculum verification software is often programmed to select resumes with specific keywords, so even if you want to add synonyms for common keywords in Data Science, this should work. But for that, reading the job description is very important.
Sometimes, including keywords in the curriculum also helps. Include keywords in the body of the curriculum, making sure they match the most important keywords and skills mentioned in the job description.
A simple adjustment can be the key to success. Say the same thing, but in a different way.
3. Indicate Your Knowledge in the Necessary Skills
A recruiter wants to see if the candidate has really done anything for the job he or she is applying for. Skills, projects, courses or certifications in the exact lines of what is being asked for in the vacancy, can enhance your resume in the crowd. Remember: most people send the curriculum without any preparation or care and you just have to do a little more to get results. Just a little bit more. Take some time to do this. Your life and professional career are grateful.
Formatting the curriculum is an essential part. When it comes to emphasizing your job, the resume should state exactly why the recruiter should consider you for the position.
A Data Scientist's curriculum should demonstrate how good you are at managing data and your social skills emerging from projects.
A Bank of America recruiter pointed to the projects section of a candidate's curriculum and said: “If you have worked to improve your level of aptitude for a particular skill, domain or technology, it will definitely be evident on your resume. This is what we are looking for; a constant learning and implementation cycle. ”
That is why we emphasize the importance of building a portfolio of projects. Many people have no experience in Data Science and feel that companies have an obligation to hire anyway. Why not invert? Get your experience on your own, participating in competitions, volunteer projects, research projects, etc ... This does not depend on anyone, only you. Put this experience on your resume making it clear how it was obtained and your proactivity. You will be in the 2% group, those who really take care of your career. This can get the recruiter's attention.
4. Your X Factor
People, in general, always consider it foolish to include a section of awards and hobbies in the curriculum, instead of a complete opening statement, the famous summary.
Choose not to run away from the unique interests and achievements you have. In addition, being different causes an effect inherent to human beings: curiosity.
Examples of awards and hobbies that could be added to your resume: stock trading on the stock exchange, blogs and Indian classical dance and more. There is a high probability that during most interactions with recruiters or interviews, they will ask you to talk more about it, either out of curiosity or to assess your passion for what you do. Recruiters aren't just looking for advanced technical knowledge. They want people, before anything else.
Examples:
- In the interview with a company, the recruiter herself was trained in the same dance form as the candidate and had a conversation for 3 considerable minutes, which is substantial in an interview.
- Another recruiter asked what shares on the stock exchange the candidate had so far because he was looking for a new stock to invest.
If the candidate looked after your resume with care, at least it won the recruiter's attention. This is already very valuable in a recruitment process.
These sections are not just to differentiate yourself from other candidates, but to introduce a new tangent of conversation with recruiters, something that shows far beyond what is described in your resume.
5. List Your Achievements
Not all recruiters are experts in the field for which the selection is being made, unless it is a targeted information session or a differentiated hiring process.
Long points about what you did on a project definitely hurt!
For example, for a Machine Learning project that you worked on and put in your portfolio, how would you document this demonstrating to the recruiter? Here are some examples:
UNNECESSARY DETAILS FOR THE RECRUITER : I trained and optimized a machine learning model with RandomForest on a data set to predict wine quality with 99% accuracy.
OBJECTIVE AND CONCISE : I trained a decision tree model to predict wine quality with 99% accuracy.
The key is to be concise, to the point. Avoid implicit or redundant phrases, rearrange detailed phrases, or avoid being too wordy. Then, emphasize the numbers or metrics on your resume in bold to show the impact you were able to make, rather than just mentioning superlatives.
SUPERLATIVE USE (BAD) : significantly improved conversion rates and drastically reduced bounce rate using an SVM model.
CLEAR AND INFORMATIVE (BOM) : I doubled the conversion rates from 0.5% to 1% and reduced the bounce rate by 18% using an SVM model.
Small but substantial changes to your resume definitely help to gain more visibility and interaction. In the Data Scientist Career Preparation course we teach how to put together a curriculum and offer a template to help students.
