We all know that Data Science is an umbrella term. Data Science team needs a group of individuals who are Picasso of their own arts, as there is a diversity of perspectives. Otherwise, your project would be at risk.
So if you are about to build the team for your Data Science Project, this post is for you. We will tell you about the top 10 roles in AI and Data Science, and the order in which you have to build up this team. It would definitely be useful for you if you are looking for making your data used with the help of decision intelligence engineering approach.
1. Data Engineer
Yes, we will start from zero as the business always starts from zero. It makes perfect sense, as you need to get data before going to Data Analysis. In case you are handling small datasets, data engineering is actually entering some numbers into a spreadsheet. However, when you are operating at impressively large scale, same data engineering turns into a sophisticated discipline in its own right. Someone should be responsible for dealing with tricky engineering aspects of delivering data that the rest of the team can work with.
In short, Decision-making skills have to be in place before a team can get value out of data.
Ensure that you have a decision-maker who understands the art and science of data-driven decision-making before you hire PhD-trained data scientist. It should be this person’s duty to identify decisions worth making with data, frame them (everything from designing metrics to calling the shots on statistical assumptions) and determine the required level of analytical rigor based on the potential impact on the business. Opt that deep thinker who is not always saying things like that, “Oh, whoops, that didn’t even occur to me as I was thinking through this decision.” It must have occurred to him/her.
Next hire is everyone already working with you. Everyone can get inspired with data but one thing they may lack in is familiarity with software that is well suited for the job. Being a Data Analyst is not rocket science.
If you learn R or Python, it is just like an upgrade over MS Paint for data visualization. They are nothing more than versatile tools for looking at a wider variety of datasets than just red-green-blue pixel matrices. You have done data visualization and analytics if you have ever looked at a digital photograph.
Even if you feel exhausted after looking at the first five rows of a spreadsheet that would also be enough. In case the entire workforce is empowered to do that, you will have a much better finger on the pulse of your business than while no one is actually looking at any data at all.
However, remember that it is data and anything you conclude, you should conclude it wisely. Without specialist training, it is not recommended to conclude anything beyond your data.
4. Expert Analyst
Now comes the stage of entering the lightning-fast version. This person can look at more data in less time. The game is speed, exploration, discovery and ultimately fun. It is not the role concerned with rigor and careful conclusions. Rather, this person helps your team get eyes on as much of your data as possible. The result is that your decision-maker can get a sense of what is worth pursuing with more care.
This may sound counterintuitive, but do not staff this role with your most reliable engineers who write gorgeous, robust code. The job 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.
We got this entire staff for exploring this data, now we need someone to control the data flow. In other words, someone should put a damper on a feeding frenzy. Data types often send common sense out the window. It would be wise to have someone around who can prevent the team from making unwarranted conclusions. Therefore, Statisticians are the only people who can help decision-makers come to conclusions safely beyond the data. This careful approach is necessary.
Let us suppose that your machine learning system worked in one dataset, all you can safely conclude is that it worked in that dataset. How do you know that it would work when it is running in production? Should you launch it or not? You will need some extra skills to deal with those questions. Statistical skills definitely.
6. Applied Machine Learning Engineer
The best attribute of an applied AI / machine-learning engineer is not an understanding of how algorithms work. It rather is to use them and not build. That basically is what researchers do. Expertise at wrangling code that gets existing algorithms to accept and churn through your datasets is what you need.
Besides quick coding fingers, look for a person who can bravely face the failure, and has the courage to stand up once again. You almost never know what you are doing, even if you think you know. You run the data through a bunch of algorithms as quickly as you can and check if it works. A reasonable expectation is that you will fail a lot before you succeed. A large part of the job is dabbling blindly, and it takes a particular kind of personality to enjoy that.
You cannot know in advance that what will work, as your business problem is not in a textbook. So do not expect to get perfect result on the first go. Just try many approaches as quickly as possible and iterate towards a solution. When you say, “running the data through algorithms”, what actually do you mean by data here. Of course, it means the inputs your analysts identified as potentially interesting. Now you know analyst is to be hired at early stages. The machine-learning engineer must have a deep respect for the part of the process where rigor is vital.
7. Data Scientist
A data scientist is someone who is a full expert in all of the three preceding roles. Remember that not everyone uses the same definition. You will 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 comes at number 6 because hiring true three-in-one is an expensive option. If you can hire one within budget, it is a great idea. However, if you are on a tight budget, consider upskilling and growing your existing single-role specialists.
8. Analytics Manager/Data Science Leader
These people are hybrid between the data scientist and the decision-maker. Their presence on the team acts as a force-multiplier. They ensure that your data science team is not off in the weeds instead of adding value to your business. They are the goose who lays the golden egg.
Unfortunately, these force multipliers are rare. If you are lucky, enough to hire one of these, then never let them go. “How could we design the right questions? How could we make decisions? How could we best allocate our experts? What is worth doing and what is not? Do the skills and data match the requirements? How could we ensure good input data?” This kind of questions does not let them sleep peacefully.
9. Qualitative Expert/Social Scientist
If your decision maker is unskilled, he might be very dangerous for you. Despite the fact that he is a brilliant leader, manager, motivator, influencer, or navigator of organizational politics. Augment them with a qualitative expert. The qualitative expert can supplement his skills.
Social Expert typically, has social science and data background. He can be a behavioral economist, neuro-economist, and JDM psychologist. Job Role is to help the decision maker clarify ideas and examine all the angles. He can turn ambiguous intuitions into well-thought-through instructions in a language that is easy for the rest of the team to execute on.
Hiring managers often think their first team member needs to be the ex-professor, but actually, you do not need those Ph.D. folk. Not unless you already know that, the industry is not going to supply the algorithms you need. Most teams will not know that in advance, so it is wise to do things in the right order.
In The Nutshell
Depending on the nature of your very own project, it is perfectly fine to tailor this list according to your needs. Your project does not necessarily need all of these people. So only, hire the people you need and build a good team as soon as possible. However, you should follow the order we have explained. If after hiring the first five people, you feel that the team is adequate, do not hire more people.