Here is an elegantly simple highlight of how the data science profession is changing. In April 2008, there was still some debate around naming of the field now called data science. Long and short of it, below is the original LinkedIn job posting of the data science role. It is gorgeously concise:
Be challenged at LinkedIn. We’re looking for superb analytical minds of all levels to expand our small team that will build some of the most innovative products at LinkedIn
No specific technical skills are required (we’ll help you learn SQL, Python, and R). You should be extremely intelligent, have quantitative background, and be able to learn quickly and work independently. This is the perfect job for someone who’s really smart, driven, and extremely skilled at creatively solving problems. You’ll learn statistics, data mining, programming, and product design, but you’ve gotta start with what we can’t teach – intellectual sharpness and creativity.
This is in sharp contrast to LinkedIn’s latest data science posting in June 2015. In the interest of space, the font is small and each skill that can be tested in an interview is highlighted by green bullets:
Let’s do some arithmetic. If each bullet is a one semester course, an apt pupil could think of applying in 2.5 years. That assumes you can fit machine learning into one course. With job descriptions changing on a monthly basis and skills requiring years of development, becoming a data scientist is a moving target.
Now, what causes this? In my opinion – this is not a fact – ‘feature creep’ of required skills listed in today’s job postings is the result of good intentions:
[Boss] Good job on project X! How did you do it?
[Boss] You’re talented! We want more projects like X in the future. I’ll add those skills to the next job description.
The question missing from the conversation is, ‘what skills did the data scientist have before they started work on Project X?’ I’m willing to bet a number of successful data science projects are done by practitioners who more closely match the first description than the second.
So how do aspiring data scientists hit this moving target? Understand the motivations behind where the target is! The goals of the positions, while 7 years apart and written by different people, are strikingly similar.
build some of the most innovative products at LinkedIn [to hire, find contacts, stay in touch, and manage their professional brand]
developing ways for members to improve their professional lives
That’s the goal – advance the mission. Now, this is done in slowly and systematically by talented people with many skills. But every so often, I feel inspired when someone with “intellectual sharpness and creativity” challenges the imagination.
Above are opinions, for facts on how data science is changing please see Evolution of Data Science.