Brian G Birkhead
Brian is a former Statistics and Maths academic at Warwick University and UCL. He is an award-winning consultant with 20 years’ experience supporting organisations of all sizes and in all sectors, to develop, implement, and evaluate insight-led business and marketing strategies.
He currently works with EDIT as an advisor to our expanding Data Science Team.
In recent years Data Analysis has morphed rapidly into Data Science, as new technologies and AI have developed and gained traction, and as machine learning has begun to supplant more directed, human-interventionist modelling methodologies.
Consequently, Data Science is now one of the fastest growing disciplines across all sectors of commerce.
Universities are producing legions of graduates with strong technical skills in the disciplines of mathematics, statistics, coding, and machine learning. For these new entrants to the world of work, marketing and technology businesses represent an attractive career path. Starting salaries are considerably higher compared to research or academia, and the growth in artificial intelligence usage, combined with the ability to capture and report on every touchpoint of the modern consumer journey ensures their roles will remain prominent for many years to come.
The lexical shift in job title from “Data Analyst” to “Data Scientist” is designed to reflect these trends in technology and business, and to elevate the perceived status of new recruits in line with the increased contribution they can now make.
The use of the word “Scientist” is rightly intended to reflect the high-grade work that these new brains are expected to do – after all, “science” carries with it the admirable connotations of rigour, discipline, progress, and intelligence that somehow “Analyst” just fails to do.
But there is a danger that this re-labelling will encourage new recruits to live up to the image of being “boffins” and for them to be viewed as such by others in the businesses for which they work.
However, boffins are the last thing a business needs given that most of the real-world, commercial applications they are called on to support demand equal measures of intuition, intelligence, and technical prowess.
During my work with Edit I have witnessed how they, and other astute organisations, avoid the trap of recruiting people based solely on high levels of technical skills, whilst overlooking the importance of those recruits possessing the potential to develop commercial nous, to exercise pragmatism and to use business intelligence – for these are the attributes essential to appropriately framing/conceptualising their models and to turning model outputs into actionable, explanatory insights.
Edit’s joint Managing Director Rob McGowan is himself a trained Data Scientist, he comments,
“As a result of my background I have a real appreciation for the rigorous academic skills required to make it as a Data Scientist, but in the real world that’s only half the story. At Edit Data Scientists need to be able to talk the same language as their clients and internal stakeholders. They need to communicate their insight and learnings in both understandable and convincing terms if they are to affect the strategic and tactical changes that their findings imply and to truly (not just theoretically) optimise performance.”
Therefore, my advice to anyone looking to develop a commercial career in data science is to consider themselves as moving into the field of consultancy, it’s not just the insights you gather but your ability to effectively convey them which will make all the difference.
