Introduction to Resume Building Guide for Data Scientists
Bored of applying for dozens of jobs but not getting any response? Want to build an excellent resume that stands out against other applicants? Your resume may just seem like another piece of paper - a boring document. You think it's a small part of your application. But without an impeccable resume, nothing else matters!
In this article, we have created Resume Building Guide for Data Scientists and some key elements your resume needs to have. Does your resume tick all the boxes? Let's find out!
General Tips for an Amazing Resume
The 3 C's - Crisp, Clean & Clear
The most important thing you need to keep in mind while writing your resume is to keep it short. An ideal resume contains everything the company needs to know about you - on ASINGLE page. The average time a recruiter takes to review a resume is said to be 6-8 seconds! Shocked? You can look up the facts but there is no denying this. Employers receive gigantic volumes of resumes every day. So even though you have the most innovative project or an exemplary research publication - you need to know how to present your resume so that it pops out.
Customize according to the Organization
While most of us are guilty of creating a "one size fits all" resume, the idea doesn't affect your chances obviously. However, tweaking your resume to adapt to your company's demands indicates the extra effort you made in order to make yourself eligible. It may take a few minutes off your hand, but adding minor details keeping in mind the prerequisites and requirements of the job role will definitely make an impressionable mark on your employer.
Do a little research about the company like checking out their website, and discover what you can offer to them via the job role. A tailored resume separated you from more than 50% of applicants who use basic ones. Some smaller companies and start-ups may have an open and easy-going environment as well. You can try to reflect the preferred style and tone of the enterprise on your resume as well.
Keep it Smart and Professional
Keeping a resume professional means keeping the following things in mind -
- Avoid long paragraphs, split items like your summary, work experience description, and education into bullet points.
- Use the Google X-Y-Z formula. This is very effective in making a point come across effectively to your recruiters. Suppose you want to mention your participation in Kaggle competitions. Don't simply mention your rank, designation, or no. of competitions you took part in. Instead, mention something out of the ordinary like -
“Participated in a Kaggle competition (X) and finished at 8th position out of 1100 teams (Y) as a team of 3 to build a deep learning neural network (Z)”
"Ranked as Kaggle Master (X) for my excellent contribution to the data science community (Y) by contributing datasets, participating in discussions and sharing relevant notebooks (10 + silver medals) (Z)
- Only mention skills and certifications relevant to the job role. For instance, if the company has no mention of Power BI in its technical stack or job requirements, then there is no need to mention certification (unless it's a data analyst or business analyst role).
- And last but not the least, no typos! Use correct grammar and spelling. Additionally, using active voice also keeps content shorter and readable.
Include Contact Details
- Never forget to add your current, working phone number and professional-looking email address.
- Mention the city and state you live in. There's no need to mention the entire physical address.
- Your LinkedIn profile link should also be somewhere at the beginning of your resume. Try to compress it using URL shorteners.
- If you have a portfolio website or a GitHub profile, make sure you include it in your resume. This indicates to employers the kind of projects you have worked on.
Prioritize the information you wish to convey
In the following sections, we are going to discuss the elements important to display as a data scientist. However, it is also important to note the order in which you put forward each element. This additional step is extremely crucial and ensures that you portray yourself as per the company's suitability and leave out irrelevant information.
For instance, in the skills section, make sure that you mention your technical skills before your soft skills. Also there may have been several awards you have achieved as a professional or student. Make sure the relevant ones are highlighted first. Some of you may additionally come from varied backgrounds like electronics, sales, mechanical etc. You may want to skip elaborately explaining your previous work details.
Important Elements in an Aspiring Data Scientist's Resume
A typical resume is divided into the following sections -
The work experience section is the most important aspect of your resume. It is what defines your past professional career. It's critical that you make this part of your resume impactful.
- Include only major and relevant positions. For eg., it's okay if you come from a financial marketing background as long as you have relevant skills to portray i.e analysis of market data, stock predictions etc. If you come from a mechanical background, mention the supply chain management work you undertook.
- Add work experience in a reverse chronological order, with your last gig appearing topmost.
- Put yourself in your employer's shoes. What does he want to know about your prior work experience - the responsibilities you undertook or the impact you made to the organization? Obviously the latter! So explain how you contributed to the organization rather than just listing down responsibilities.
Don't have experience? Don't sweat! We have some recommendations for you as well.
- Make sure to include academic courses that included relevant project work i.e subjects like advanced databases, big data analysis, data warehousing etc.
- Mention your internships. Even if they don't necessarily line up with the job you're applying for. Soft skills matter too!
- If you don't have practical experience, create some. Work on personal projects and mention the algorithms and techniques you used to come up with your insights.
This is especially relevant to those just making their mark in the data science world. Entry level data scientists should be careful in adding the following information -
- University and major.
- GPA, Pointer, percentage - whatever your particular university provides.
- Courses relevant to the job you're applying for
- Student chapters that you were a member of.
- Exemplary awards you received in academics.
Data scientists are hot on the demand scale in recent times. They need to possess a unique set of skills - technical and soft ones alike. Recruiters and hiring managers of larger firms makes use of resume scanners to look out for keywords. So make sure you highlight the ones especially mentioned in their job description requirements.
|Technical Skills||Soft Skills|
|Programming languages like Python, R, Scala etc.||Critical thinking, cognitive and problem-solving skills|
|Working with databases - NoSQL (MongoDB) and SQL RDBMS systems||Ability to take up new projects. Adaptibility to work across different domains.|
|Data visualization software - Tableau, Power BI, Alteryx, Qlikview||Understanding of business operations . Leadership skills|
|Big data tools like Apache Spark, Hadoop||Storytelling skills in the form of visualizations|
Remember, mentioning your skills only provides a brief abstraction of your data science qualifications. Only your prior experience, projects and certifications are proof of them. So ensure that they tie up neatly with the skills you mention.
Projects, Publications & Certifications
It's not just important to develop projects and implement models. You need to make it significant within your digital presence. Upload personal projects on GitHub profile. Enhance your Kaggle profile by participating in competitions and contributing notebooks to the data science community. You can share your achievements and projects on LinkedIn to make your recruiter realize how you're open to feedbacks and how often you play around with data science models.
For instance, working with unstructured data is an appreciable task in the domain. So try working on projects that deal with messy data from websites, images, videos, tweets - pick your poison!
Now when we say publications, it's not mandatory that you have it published on an esteemed journal somwehere. A publication could be an article explaining why a certain machine learning model is best suited for the given problem statement, or a case study research on a famous dataset.
Publications contribute to the soft skills you mention in your resume. It indicates your ability to clearly communicate complex ideas.
Resume Building Guide for Data Scientists, As human beings, one thing we seek throughout our lives is "validation". Certifications are the validation for a data scientist's skills! If you're someone who's trying to make a career switch into data science, certificates are a must-have on your resume! You can shorten your education section to accommodate all the certificates you have garnered.
Some popular data science up-skilling certificates worthy of mentioning are -
- Certified Analytics Professional (CAP)
- Microsoft MCSE: Data Management and Analytics
- Microsoft Certified Azure Data Scientist Associate
- SAS Certified Advanced Analytics Professional
- SAS Certified Big Data Professional
- Certified SAS Data Scientist
- Data Science and Statistics Certification by MIT (edX)
- IBM Data Science Professional Certificate (Coursera)
Summary of Resume Building Guide for Data Scientists
Phew! That was surely a lot of information to grasp to build a one-page resume. Let's explore a few key takeaways from this article -Resume Building Guide for Data Scientists
- Mention all your skills and expertise - just keep it concise and separated in sections.
- Show off your practical knowledge by adding projects and work experience (internships if you don't have any).
- Ensure your skills align with the requirements of the job role. Also, We have a Specialization Course In Data Science Masters Program To Make You Industry Ready Data Scientist.