Top 10 roles in AI and data science
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not!
Google’s Geoff Hinton is a hero of mine and an amazing researcher in deep learning, but I hope you’re not planning to staff your applied data science team with 10 of him and no one else!
Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience.
If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.
We start counting at zero, of course, since you need to have the ability to get data before it makes sense to talk about data analysis. If you’re dealing with small datasets, data engineering is essentially entering some numbers into a spreadsheet. When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with.
Before hiring that PhD-trained data scientist, make sure you have a decision-maker who understands the art and science of data-driven decision-making.
This individual is responsible for identifying decisions worth making with data, framing them (everything from designing metrics to calling the shots on statistical assumptions), and determining the required level of analytical rigor based on potential impact on the business. Look for a deep thinker who doesn’t keep saying, “Oh, whoops, that didn’t even occur to me as I was thinking through this decision.” They’ve already thought of it. And that. And that too.
Then the next hire is… everyone already working with you. Everyone is qualified to look at data and get inspired, the only thing that might be missing is a bit of familiarity with software that’s well-suited for the job. If you’ve ever looked at a digital photograph, you’ve done data visualization and analytics.
Learning to use tools like R and Python is just an upgrade over MS Paint for data visualization; they’re simply more versatile tools for looking at a wider variety of datasets than just red-green-blue pixel matrices
And hey, if all you have the stomach for is looking at the first five rows of data in a spreadsheet, well, that’s still better than nothing. If the entire workforce is empowered to do that, you’ll have a much better finger on the pulse of your business than if no one is looking at any data at all.
The important thing to remember is that you shouldn’t come to conclusions beyond your data. That takes specialist training. Just as with the photo above, here’s all you can say about it: “This is what is in my dataset.” Please don’t use it conclude that the Loch Ness Monster is real.
Enter the lightning-fast version! This person can look at more data faster. The game here is speed, exploration, discovery… fun! This is not the role concerned with rigor and careful conclusions. Instead, this is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what’s worth pursuing with more care.
This may be counterintuitive, but don’t staff this role with your most reliable engineers who write gorgeous, robust code. The job here is speed, encountering potential insights as quickly as possible, and unfortunately those who obsess over code quality may find it too difficult to zoom through the data fast enough to be useful in this role.
I’ve seen analysts on engineering-oriented teams bullied because their peers don’t realize what “great code” means for descriptive analytics. Great is “fast and humble” here.
