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? I hope not!
#6 Data Scientist
The way I use the word, a data scientist is someone who is a full expert in all of the three preceding roles. Not everyone uses my definition: you’ll see job applications out there with people calling themselves “data scientist” when they have only really mastered one of the three, so it’s worth checking.
This role is in position #6 because hiring the true three-in-one is an expensive option. If you can hire one within budget, it’s a great idea, but if you’re on a tight budget, consider upskilling and growing your existing single-role specialists.
#7 Analytics Manager / Data Science Leader
The analytics manager is the goose that lays the golden egg: they’re a hybrid between the data scientist and the decision-maker. Their presence on the team acts as a force-multiplier, ensuring that your data science team isn’t off in the weeds instead of adding value to your business. This person is kept awake at night by questions like, “How do we design the right questions? How do we make decisions? How do we best allocate our experts? What’s worth doing? Will the skills and data match the requirements? How do we ensure good input data?” If you’re lucky enough to hire one of these, hold on to them and never let them go. Learn more about this role here.
#8 Qualitative Expert / Social Scientist
Sometimes your decision-maker is a brilliant leader, manager, motivator, influencer, or navigator of organizational politics… but unskilled in the art and science of decision-making. Decision-making is so much more than a talent. If your decision-maker hasn’t honed their craft, they might do more damage than good.
Don’t fire them, augment them. You can hire them an upgrade in the form of a helper. The qualitative expert is here to supplement their skills.
A person typically has a social science and data background (behavioral economists, neuroeconomists, and JDM psychologists receive the most specialized training, but self-taught folks can also be good at this). Their job is to help the decision maker clarify their thoughts, examine all the angles, and turn ambiguous intuitions into well-thought-through instructions in language that makes it easy for the rest of the team to execute on.
The qualitative expert doesn’t call any of the shots. Instead, they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker. Having them on board is a great way to ensure that the project starts out in the right direction.
Many hiring managers think their first team member needs to be the ex-professor, but actually you don’t need those PhD folk unless you already know that the industry is not going to supply the algorithms that you need. Most teams won’t know that in advance, so it makes more sense to do things in the right order: before building yourself that space pen, first check whether a pencil will get the job done. Get started first and if you find that the available off-the-shelf solutions aren’t giving you much love, then you should consider hiring researchers. If they’re your first hire, you probably won’t have the right environment to make good use of them in any case. Don’t bring them in right off the bat. It’s better to wait until your team is developed enough to have figured out that what they need a researcher for. Wait till you’ve exhausted all the available tools before hiring someone to build you expensive new ones.
#10+ Additional personnel
Besides the roles we looked at, here are some of my favorite people to welcome to a decision intelligence project:
- Domain expert
- Software engineer
- Reliability engineer
- UX designer
- Interactive visualizer / graphic designer
- Data collection specialist
- Project / program manager
Many projects can’t do without them — the only reason they aren’t listed in my top 10 is that decision intelligence is not their primary business. Instead, they are geniuses at their own discipline and have learned enough about data and decision-making to be remarkably useful to your project. Think of them as having their own major or specialization, but enough love for decision intelligence that they chose to minor in it.
Huge team or small team?
After reading all that, you might feel overwhelmed. So many roles! Take a deep breath. Depending on your needs, you may get enough value from the first few roles.
Revisiting my analogy of applied machine learning as innovating in the kitchen, if you personally want to open an industrial-scale pizzeria that makes innovative pizzas, you need the big team or you need to partner with providers/consultants. If you want to make a unique pizza or two this weekend — caramelized anchovy surprise, anyone? — then you still need to think about all the components we mentioned. You’re going to decide what to make (role 1), which ingredients to use (roles 2 and 3), where to get ingredients (role 0), how to customize the recipe (role 5), and how to give it a taste test (role 4) before serving someone you want to impress, but for the casual version with less at stake, you can do it all on your own. And if your goal is just to make standard traditional pizza, you don’t even need all that: get hold of someone else’s tried and tested recipe (no need to reinvent your own) along with ingredients and start cooking!