The #1 Reason Your Projects Get Ignored

To get a Data Science Job, I used to think you could complete a project and leave the code sitting in a Github repos.

This is how I got my first couple of Data Science jobs. But this was 5 years ago in an easier job market. I was also a student, and the requirements for getting a job were lower.

In my boring Tax job, one benefit was I got to build a Machine Learning model. But I was frustrated that we never got to use the model to make decisions (one of the reasons I left).

Using a solution to a problem (e.g., a ML model) to produce change is known as putting something ‘into production’.

To prepare for my interview I’d revised my model inside out.

Whilst the interviewers were impressed, they said ‘that’s cool but have you put anything into production?’

I started sweating, heart-pounding, knowing a bad answer would leave me receiving a rejection email and returning to my current job of meaningless work and lame colleagues.

Lucky for me, I’d done my interview prep and even though it wasn’t the sexiest project, I talked about how I’d sped up my company’s internal audit process.

They were impressed, and I got the job.

It made me so happy to have exciting work, with people I vibe with, I set my phone wallpaper to the company logo.

And the 45% pay rise wasn’t bad either 😉.

That project on it’s own wasn’t enough – I’d already done a class Tailored Project and dazzled the interviewers with my charm – but it certainly helped.

If I didn’t have the audit production project, I might have lost out to someone who did. Or to someone who was more Tailored than me, e.g., someone with experience in the industry.

Because that’s what Data Science is all about – using data to help your customers (and make more money) or to make life easier (by saving time).

You might care, but Execs aren’t interested in your snazzy Juypter notebook where you’ve used your new Matplotlib course to make some nice histograms.

They care about solving the problem. Delivering good outcomes. And producing more dollar.

If you don’t put projects into production, you’ll keep asking yourself why your applications are being ignored. You’ll remain in your dead-end job with no career progression, whilst you could be making good money by working on projects which make you grow.

So that’s why Production is the third pillar of Tailored Projects.

For example, my client Diaa built a dashboard to analyse how different foods affect his body and used this to decide what to eat.

If he just analysed data on different foods, nobody would care. But by going the extra mile and showing how his work can make him healthier, he’s showing that he GETS what  Data Science is about.  

And interviewers love hearing him talk about it.

So don’t just build projects. Put it into production, and make it a Tailored Project.