I have spent the last three months unpicking the profile of the ideal data scientist, finding out where they want to work and debating whether we should scrap the term altogether but now I am exploring another issue and that involves where Data Scientists should sit. In my quest to discover what the ideal employer looks like, I came across a huge obstacle that was presenting itself to my candidate network which I believe is contributing to the high turnover of data professionals and a forever-shrinking talent pool. More and more data scientists are being mis-sold a job. This is primarily due to a lack of understanding of the industry with organisations ready to join the Data Science party without reading the invite. It’s also because companies are overselling a role as technical, exciting and hands-on when it’s a simple IT role with a trendy title. Data Scientists longing for self-development are being sat on the IT desk of a company claiming to be data-driven and quite simply, they’re bored. This has made me wonder if the turnover of Data Science professionals is really linked to where they sit in a company. If we were allowing them to sit on a desk where they will learn, develop and progress as a tech professional will they be more inclined to stay on board? Are we stunting their growth by sitting them next to people they will never technically learn from? I’d like to hear from you. Where’s the best place for a Data Scientist to sit and does it really help retain talent? Please spare 60-seconds to take my anonymous survey – all results will be used in my upcoming insight paper focused on the future of Data Science.
08 Jan 2019
I recently asked my network to describe the ideal data scientist; but, the truth of the matter is - the ideal data scientist doesn’t exist. Not because of the candidate-short market we find ourselves in or the lack of professionals old enough to hold both a MSc and PhD in Data Science or relevant field but because of the wide range of requirements organisations have for Data Science professionals. Shaun McGirr is Head of Data Science & Business Intelligence at Cox Automotive. Despite holding two BA degrees and a PhD in Political Science; on the Half Stack Data Science Podcast, he urges businesses to understand whether a PhD with multiple post-doctorates is really necessary for their needs. He believes that for 99% of them, it will be counter-productive. On the other side of the spectrum sits a company I met with recently who required a Machine Learning specialist with a PhD, post-doctorate and further doctorate from a better post-doctorate group. The person must also have no prior commercial experience as they believe you are so far from the cutting edge in a commercial environment - you essentially de-skill. There is such a contrast from company to company that there is no single definition belonging to a Data Scientist causing a whole heap of trouble for the organisations looking to find them. A perfect professional with an MSc, PhD and further post-doctorate may be flawless for one organisation but they would be deemed over-qualified and out-of-touch by others. The term ‘Data Scientist’ seems to incorporate so many varied roles and responsibilities, is it time to create more specific job titles – each with their own unique requirements?
19 Dec 2018
It’s no secret that technology is a job rich, candidate short marketplace meaning organisations can be left feeling desperate for great people and great people don’t stick around for long. Despite the high turnover of candidates and the quest to find them in the first place, clients are not holding back on their requirements for the ideal data scientist. We surveyed our network to investigate what the ideal data scientist looks like and thought it might help you if you are seeking your next adventure or searching for a data scientist but not sure where to start. Perhaps surprisingly, the ideal data scientist must have a degree in maths, physics or statistics as well as a masters or PHD in Artificial Intelligence. They will also be known to run their own projects, constantly challenge and upskill themselves and own a resume that screams self-improvement. Do you agree that a desire for self-improvement is the most attractive quality in a data-scientist or will technical ability always prevail?
26 Nov 2018
I wanted to share this HBR article as it features a data-scientist leader from Seattle who argues that communication remains a critical part of data work and that while building and using deep-learning infrastructures are important, neither are as vital as the ‘ability to learn on the fly and communicate to stakeholders.’ I believe that while communication is important, it completely depends on your level of experience. If you work for a data-centric company; as many start-ups today are, communication isn’t going to be the key attribute needed of a tech professional. There will be a CTO or CDO who will be responsible for translating the data-work with the wider business allowing data scientists to focus on their strongpoints. If it’s a massive conglomerate however, it is likely that the company will require a chief data scientist to cement their place in the business and get buy-in from shareholders - this is where communication needs to come back into play. Do you think communication is the most important skill-set of a data scientist? You can read the full article here:https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
14 Nov 2018