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As I reflect on my days as a student, I now realize that there were some crucial elements missing from my data science resume. These shortcomings probably caused me to be rejected for several jobs. Not only was I unable to present myself as a valuable asset to potential teams, I also had difficulty showing my ability to solve data science problems. However, over time I improved and collaborated with various teams to figure out what I was missing and how I could do better if I had to start over.
In this blog, I will share the 7 things that students often overlook on their data science resumes, which can prevent hiring managers from calling them for interviews.
Complicating your resume with technical terms, too much information, or unconventional formats can get you rejected right away. Your resume should be easy to read and understand, even for someone who is not deeply versed in data science. Use a clean, professional design with clear headings, bullets, and a standard font. Avoid dense blocks of text. Remember, the goal is to communicate your skills and experiences as quickly and effectively as possible to the hiring manager.
When listing your past work experiences or projects in the experience section, it is recommended to focus on measurable accomplishments rather than simply listing your responsibilities.
For example, instead of stating “Developed machine learning models,” you could write “Developed a machine learning model that increased sales by 15%.” This will demonstrate the tangible impact of your work and show your ability to deliver results.
When creating a list of your technical skills, it is essential to highlight those that are directly relevant to data science. Avoid including skills that are not related to data science, such as graphic design or video editing. Keep your list of skills concise and write down the number of years of experience you have in each one.
Be sure to mention programming languages like Python or R, data visualization tools like Tableau or Power BI, and data analysis tools like SQL or pandas. Also, his experience with popular machine learning libraries like PyTorch or scikit-learn is worth mentioning.
Data science does not depend solely on technical skills. Collaboration and communication skills are crucial. Including experiences where you worked as part of a team, especially in multidisciplinary environments or instances where you communicated complex data insights to non-technical stakeholders, can demonstrate your interpersonal skills.
Employers value hands-on experience in the data science field. If you have completed internships, projects, or research in data science, be sure to highlight these experiences on your resume. Include details about the projects you worked on, the tools and technologies you used, and the results you achieved.
Students often underestimate the power of showcasing relevant projects. Whether it's a class assignment, a capstone project, or something you created for fun, include projects that demonstrate your skills in data analysis, programming, machine learning, and problem solving. Be sure to describe the goal of the project, its function, the tools and techniques used, and the outcome. Links to GitHub repositories or project websites can also add credibility.
The field of data science is continually evolving and employers are looking for candidates who can adapt to new challenges and technologies.
As a data scientist, you can go from data analyst to machine learning engineer in just a few months. His company may even ask you to deploy machine learning models into production and learn how to manage them.
The role of a data scientist is fluid and you have to be mentally prepared for role changes. You can demonstrate your adaptability and problem-solving skills by highlighting any experiences where you had to quickly learn a new tool or technique, or where you successfully tackled a complex problem.
Creating an online portfolio and sharing it on your resume is extremely important. This will allow hiring managers to quickly take a look at your past projects and the tools you've used to solve certain data problems. You can check out the top platform to build a data science portfolio for free: 7 Free Platforms to Build a Strong Data Science Portfolio
Not including a link to your GitHub repository or a personal website where you showcase your projects is a missed opportunity.
One important thing to keep in mind when submitting your resume for job applications is to modify it according to the job requirements. Look for the skills needed for the job and try to include them on your resume to increase your chances of getting a call for an interview. In addition to your resume, networking and LinkedIn can be very helpful in finding freelance jobs and projects. Consistently maintaining your LinkedIn profile and publishing posts regularly can go a long way in establishing your professional presence.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
