The purpose of this report is to feed the “data scientist”, the collective entity, to the number crunching algorithm and understand what makes a data scientist.Reverse engineering the data scientist entails analyzing their skillset, employment history, the industry they work for, academic background, and their formal qualifications. Knowing that, the aspiring data scientist can take informed professional steps to securing the title. When we at 365 Data Science first attempted to dismantle the data scientist in 2018, we revealed a rich professional profile. Twelve months have passed since our initial research and the replicated study suggests that the field is evolving and, with it, the typical professional evolves as well.
A note on methodologyThe collective ‘data scientist’ profile was informed by a study on 1,001 professionals currently employed as data scientist. The data was collected from these data participants’ LinkedIn profiles and according to a series of prerequisites. Forty percent of the sample were currently employed at a Fortune 500 company, whereas the remainder worked elsewhere; in addition, location quotas were introduced to ensure limited bias: US (40%), UK (30%), India (15%) and other countries (15%). The selection was based on preliminary research on the most popular countries for data science, where information is public.
Is the typical data scientist of 2018 relevant in 2019?At a glance, absolutely! The domain is still strongly dominated by men (69%), who can hold a conversation in at least two languages (not to be confused with programming languages, which, if included, would at least double this number). They have been in the workforce for 8 years, but only working as data scientists for 2.3 of them. They can proudly frame up a second-cycle academic degree (74% hold either a Master’s or a PhD), and do a lot more than program “Hello World” in at least Python or R (73%), often both.
Does ‘data scientist’ imply Doctor of Philosophy?Just as the field is not impregnable by women, so is having a PhD not a prerequisite for the position. In fact, less than a third of the data scientists in the cohort hold a Doctorate degree (28%). This is a comparable number to last year’s 27%, which seems to entail that industry does not intentionally introduce an unattainable degree of academic prowess.
Level of education and work experienceFrom university, to an internship, to the final destination of a ‘data scientist’. This is the story of 8% of the data scientists in our cohort. These are the professionals for whom what it took to land the best job in the USA, was one internship position and a Master’s degree (71%) or a Bachelor’s degree (18%).
Two men and a woman walk into a room: who will be the next data scientist?
- The academic researcher
- The IT specialist
- The intern
Should I study Computer Science and Mathematics or can I learn Botanics and still make it as a data scientist?Alright. To become a medical practitioner, you would go to medical school; to become a lawyer – law school; police officer – a special academy, and so on. Data science schools are scarce as of the time of writing, if existent at all, so what do data scientists study? As a matter of fact, a respectable chunk of our cohort studied data science and analysis but a note on notation before we proceed. Due to the massive amount of unique degrees available for academic pursuit, we clustered them into seven areas of academic study.
- Economics and social sciences, which includes studies pertaining to economics, finance, business studies, politics, psychology, philosophy, history, and marketing and management
- Natural science, including physics, chemistry, and biology
- Statistics and mathematics, consisting of statistics, and mathematics-centred degrees
- Computer science, which excludes machine learning
- Data science and analysis, which includes machine learning
- Other, where you can find Art and Design, Atmospheric Science, and… others