Get blogs, industry news, updates, and how-to articles delivered right to your inbox. We email with curated topics just for you. No spam, just the good stuff.
Lately, I have been speaking to a number of people who are convinced that SAS’ days are numbered. Open-source technologies and tools are increasingly being chosen over enterprise solutions; this was highlighted in Alex Woodie’s excellent piece for Datanami which you should check out if you get a moment: Python Eats Into R as SAS Dominance Fades.
One of the most common questions that I am asked as a Recruiter/Headhunter is how to break into certain industries/sectors as an Analytics, Data Science and Machine Learning professional. Banking and Finance are particularly interesting to many people for a whole host of reasons, and as such, I wanted to make this post relevant to this area, and I am delighted to share the insights of the deeply experienced, Elliot Noma Ph.D.
It is well documented that there is a global shortage of Data Science professionals. Especially experienced ones. The problem seems to have its origins in several areas which include;
I have recently done a few Q&A's with Chief Analytics Officer's about their journey to getting to the height of their profession however as yet, I have not done one with respect to how to become a Chief Data Scientist. This week I got lucky when I had the chance to ask Vince Darley, VP of Growth at Deliveroo a few questions on the subject. For those who are unaware, Deliveroo is a UK tech company focused on delivering your favourite restaurant meals to your door, through a combination of great software, fantastic operations and logistics, and top notch marketing.
Being a Headhunter within the areas of Analytics, Data Science and Machine Learning, I am experiencing two challenges that employers are having at the moment specifically when it comes down to hiring Data Scientists. The first is that the term Data Scientist is increasingly used as a catch-all for anyone who works with any form of data and because it is the "Sexiest job of the 21st century," according to that famous article, employers are keen to give the title out at the earliest opportunity so as both to stay relevant but also to repackage jobs of existing employees to keep them engaged and enthused.
A while back I was having a conversation with a senior Data Science professional, and he asked me why I thought his company were not able to attract and hire the best people in that particular market. My response, in summary, was along these lines;
For those who are unaware, I am a recruiter/staffing specialist within the areas of Analytics, Data Science and Machine Learning. Over the course of the past few years, I have had countless conversations regarding what the next step and ultimate goal career wise will be for professionals working within these spheres. Many of these people have cited the job title of Chief Analytics Officer as one of the several desirable positions to have, and I have recently become intrigued as to how the current crop of CAO's have ended up in their present roles and also how the job itself is evolving.