I used to work as a data science consultant in one of the Big 4 Consulting Firms, with a master's degree in quantitative economics and B.Tech from IIT Kharagpur. Having conducted numerous interviews for data science positions, I'd like to share insights on crafting an effective data science resume.
It's not just about listing experiences, skill sets, programming knowledge, and data analysis expertise. Knowing how your resume is perceived, how it is shortlisted, and how it guides the interview process is invaluable information that can significantly enhance your chances.
A good resume should always be honest. If you include false information in your resume, a skilled interviewer will likely notice. I suggest being truthful, but that doesn't mean your resume should be empty. You can still add some interesting details to make it more appealing. First and foremost, it is crucial to define your role within the realm of data science. The data science field has three distinct profiles:
1. The Modeler:
- This individual is primarily focused on the statistical aspects of data science, specialising in the development and optimisation of models.
- Responsibilities include selecting appropriate models, determining optimal data sets, deciding on the number of observations, and choosing relevant features.
2. Data Engineer:
- Specialising in the handling of data, the data engineer is responsible for tasks such as, filtering columns and rows, and managing ETL processes (extract, transform, load).
- Proficiency in SQL, Oracle, and database management is crucial, along with the ability to collaborate effectively with data scientists to enhance the overall capabilities of the data science team.
3. Implementation Person/ML Engineer:
- This role involves the practical implementation of models developed through the collaborative efforts of statistical data scientists and data engineers.
- The foundational work in statistical data science and data engineering provides the basis for implementation, which includes deploying the models.
Having understood these things, talk about your education, add interesting points like certifications, courses, and projects related to data science, data engineering, or ML engineering. Next, discuss your internships and work experiences, emphasising those that match your target skills. If you don't have a lot of skills or experience yet, try to learn about what skills you need and relate your experiences to what you want to do.
Imagine you've created models but never deployed them, and now you want to show you're a good implementation person. Explore the implementation tasks carried out in the project. Then, describe in a few lines how the model was deployed. Remember, you don't have to be an expert in everything, just be honest and aware of what happened.
Avoid creating a resume by simply copying a format; craft one that is uniquely yours. I won't delve into typical issues with CVs and resumes, such as grammar, spelling errors, and accurate numbers, as these are implicitly understood.
In case an interview doesn't go as planned, interviewers often offer one or two opportunities for candidates to demonstrate their capabilities. Remember, it's not just your resume that secures a job; it's you who will contribute to the company. Demonstrating teachability and dedication can significantly impact your performance. Create a well-rounded, truthful resume, and focus on showcasing your abilities in the competitive data science job market. Don't think about what you lack; instead, emphasise how you can market yourself effectively.
