Want to be a data scientist?

The answer seems to be everyone! I’m writing this article to differentiate between the different areas of data science and analytics (DSA). Most clients seem to want problem solvers who can use the required tools/technologies to solve business problems. Aspiring data scientists seem to want to build machine learning models to change the world!

Most DSA capabilities go through a process of data structuring, preparing for BI/Reporting, Analytics and then predictive modelling/data science. The larger, more established clients are most likely to have concentrated opportunities to purely work on machine learning/AI etc. Most of the roles in the market will require some level of data cleaning (usually using SQL), then some statistical modelling work (usually using tools like R, SAS etc) and then visualising your results (usually with Tableau, Qlikview or Power BI).

If you’re in the early stages of your career as a data scientist, take the time out to consider why you’re interested in this field. If it’s a purely theoretical interest – consider a PhD or opportunities with larger institutions or startups focused on AI/ML. However, if you’re interested in working with data to solve business problems with an open mindset on the methods/tools to do this, there will be a far wider range of options for you.

To the hiring managers/recruiters out there looking to hire, there’s one crucial piece of advice. Take the time to figure out where you’re capability is at, make sure you promote it accurately and attract the right talent with the right motives. It’s been a great month so far; I’m recruiting for roles between $70-170k across Health, Insurance, Petcare & Tech Startups. As always, feel free to contact me on Nakul@Sterning.com or 0430 327 094 if you’re looking for a new role or to build out your DSA team. A $500 referral bonus applies to most roles so feel free to share my details with your network too!