Why Data, ML and AI Projects Are Completely Useless

The introduction challenges the common belief that building projects is the key to getting a job in data science; many think a GitHub full of projects guarantees employment.

However, employers actually value a resume that quantifies past impact more than a project portfolio. According to Burtch Works, 75% of new hires are in data and analytics roles where measurable outcomes are expected. The Burtch Works report shows that 75% of 2025 data and analytics job postings are in this field. The Burtch Works report (burtchworks.com) shows that 75% of 2025 data and analytics job postings are in this field.

The 365 Data Science guide emphasizes that a strong portfolio should focus on real-world problem-solving rather than academic exercises, shifting the focus from project count to impact. 365 Data Science emphasizes the importance of a portfolio that solves real-world problems over academic exercises for hiring. 365 Data Science (365datascience.com) emphasizes the importance of a portfolio that solves real-world problems over academic exercises for hiring.

Built-In's analysis reveals that approximately 70% of jobs are filled through the hidden job market, making networking and connecting with the right people a critical part of the job search. Built In states that 70% of data science jobs are filled through the hidden job market, not public postings. Built In (builtin.com) states that 70% of data science jobs are filled through the hidden job market, not public postings.

A Towards Data Science example shows that resumes highlighting quantified achievements (e.g., '20% increase in efficiency' or '15% reduction in processing time') are far more effective than merely listing technologies used. A Towards Data Science example shows that quantified achievements on a resume (like a 20% increase) are far more effective than merely listing technologies. A Towards Data Science (towardsdatascience.com) example shows that quantified achievements on a resume (like a 20% increase) are far more effective than merely listing technologies.

Maintaining an active LinkedIn profile, engaging with industry leaders, and contributing to relevant groups increases visibility and uncovers job opportunities that aren't publicly advertised.

Interview techniques matter: using the STAR method (Situation, Task, Action, Result) to structure your past experiences allows you to demonstrate performance during the hiring process.

For experienced professionals, leveraging prior experience with financial impact (e.g., '340K USD salary offers') carries more weight in hiring decisions and reduces reliance on project work.

For beginners, undertaking paid or unpaid real-client projects on platforms like Fiverr or Upwork provides both skill development and the real-world experience employers seek.

Ultimately, focusing on networking, resume improvement, and interview preparation can lead to a data science job within three months, whereas spending time on projects may delay this outcome.