Resume Do’s and Don’t for AI and ML jobs

How to craft your resume for AI, ML and Data Science jobs

The 2nd Episode of AIQ is out now which talks about different tips and tricks to consider while drafting your resume for a AI role application.

AIQ Ep.2 : Resume Do’s and Don’ts for AI jobs

The summary for the podcast is available below

Host: What are the major sections required in an AI profile?

Guest: The major sections include the summary, education, projects, achievements, and relevant certifications, such as those from DataCamp or Andrew Ng’s machine learning course. For beginners, Kaggle projects and side projects are also crucial.

Host: How should we write the summary? What are the major things to highlight in that?

Guest: The summary should be a 2–3 liner that captures all the highlights and key points of the resume without any filler text. It should succinctly convey the person’s achievements and experience.

Host: If somebody is trying to break into an artificial intelligence profile and has a lot of experience in another domain, should they include it or exclude it?

Guest: Definitely include it. It shows valuable work ethics and professional skills. Even if the experience isn’t directly related to AI, it still demonstrates domain knowledge and relevant professional experience.

Host: What should be more preferred in a resume: certifications or projects?

Guest: Projects should be given more weightage than certifications. Projects demonstrate practical experience and skills, while certifications show baseline knowledge. Both are important, but projects tend to stand out more in evaluations.

Host: Should the design of the resume be professionally designed or created using common software like Microsoft Word?

Guest: Focus on the clarity and crispness of the language. Avoid long paragraphs and mention metrics and results to highlight achievements. Provide links to GitHub or Kaggle profiles, and include any extra achievements like hackathons or competitions. The design doesn’t have to be professional, but it should be clear and concise.

Host: Would you recommend using any ATS-free resume checkers that give a score of the resume?

Guest: Yes, Resume Worded is a great tool for getting a score and feedback on the resume before applying.

Host: For an absolute beginner, how should they go about choosing unique projects?

Guest: Avoid very common projects and explore new areas like generative AI, NLP, time series, and reinforcement learning. This shows a willingness to learn and helps differentiate the resume from others.

Host: What should be the exact process to come up with unique ideas, and where can they do genuine research to come up with these ideas?

Guest: Kaggle is highly recommended. It offers thousands of datasets, problems, and ongoing hackathons, providing a great platform to explore and come up with unique project ideas.

Host: Should someone customize their resume according to the role or have one resume for all roles?

Guest: Not really as most roles leads to the same road and in the end of the day, everyone, be it Data Scientist or MLE needs to do the same jobs (mostly). But yes, if it takes you lesser efforts, just do it and also you can tweak a bit with the projects depending upon the Job Description.