Always Create Three Résumé for Data Science Jobs

Last Updated on September 23, 2021 by Editorial Team

Author(s): Abid Ali Awan

Careers

If you want to get noticed fast by an employer always create three different resumes for different regions of expertise.

Introduction

The data science world is not easy to get started in and employers feel skeptical about hiring data scientists in general. The data scientist performs analysis, data engineering, machine learning, and deployment, so employers don’t know what values they will be getting by hiring you or where do we this job fits in. In comparison with data analysts, they know the potential employee will be performing analysis, creating reports, and dashboards. If you want to increase your chance of getting hired faster, you should create resumes for three different regions. If you are not looking to work remotely then create resumes for three different expertise, for example, Machine learning engineer, Data Analyst, and General Data Scientist.

Why do we need multiple resumes?

Janice Reed was fully prepared for her job application and her resume was perfect, but she got reject just because her resume was not fit a specific type of job.

“This resume is good — it’s great, actually!,” he corrected, “I just don’t think it’s great for this type of job. Do you have another you can send?” — Jobcase

The employees don’t have time to look at your achievements or projects. They might reject you on the first look as your resume looks quite generic. To get better Janice started creating multiple resumes and eventually got her dream job.

I had a similar experience with job hunting as I was transitioning from technology management to a data science job. It was a quite disappointing journey as I knew I have all the certificates and projects to prove that I am the best candidate for the job. This is where my career counselor Jeff Winchell thought me an important lesson on how the USA, Europe, and South Asian countries have a different systems of writing the perfect resume. In the USA people brag about their achievements and use PAR (Problem, Action, Results) system, whereas in Germany they are quite conservative about achievements. He explained how some Asian countries prefer a CV (curriculum vitae) that is more than one page, a detailed description of your past experiences and projects.

Todo List:

If you are looking for remote work, always create three resumes for three distinct locations.

1. USA: Create a one-page resume and use the PAR system to write job descriptions.
2. Europe: Create a simple resume with simple tasks, don’t use tone to oversell yourself. Be humble with your achievements.
3. South Asia: Write a 3+ page CV, add all your experience, projects, and achievements. If you don’t have enough experience, write about your personal projects.

If you are targeting a single country, first research about what type of resumes are acceptable and then write three resumes for a different expertise.

1. Data Analyst: Remove all irrelevant projects. Write experience related to data analysis and dashboarding. Write about your experience as a data analyst and personalizes your entire resume.
2. Machine Learning Engineer: Write relevant experience and try adding more projects or models that you have deployed.
3. Data Scientist: Create a generalized resume with some projects from data engineering, analysis, and machine learning. Write about your blogs or research papers. To make it stand out write about data science certifications.

Final Thoughts

My strategy of creating three resumes has proven successful as employers were contacting me all over the world on daily basis. I know the world of data science is unfair, but you can learn few rules to stand out and get notices by big organizations. If you are focusing on one company, I will suggest you prepare multiple resumes for multiple positions. This will improve your chance of getting hired. I will also suggest you get career coaching from an expert who has been traveling all around the world for work.

You can follow me on LinkedIn and Polywork where I post amazing articles on data science and machine learning.

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