November 21, 2022
 min read

The Data Science Dream Team

As more companies scale AI projects, turning proof-of-concepts into drivers of business transformation, a clearer picture of what it takes to succeed with real-world.

As more companies scale AI projects, turning proof-of-concepts into drivers of business transformation, a clearer picture of what it takes to succeed with real-world AI is taking shape.

When it comes to AI teams, a broader set of skills are required than previously known, with a particular need for people with experience in operations and in translating AI concepts into business terms and vice versa. In other words, AI success no longer hinges on just a group of data scientists anymore.

Here is a look at how several organizations are assembling AI teams to solve business issues — and how advances in AI technology are changing the baseline skills necessary for success.

AI, a team sport

Every successful AI initiative requires a marriage between a Data expert and a domain expert.

The Data expert knows the machine learning toolkit: Which models are most likely to solve a particular problem? How do we tune a specific model to improve the accuracy of the results?

On the other hand, the domain expert brings domain-specific knowledge: what data sources are available, how (un)clean is the data, what is the quality of the models recommendations? The input from the domain expert is vital in these, as the Data expert cannot possibly answer these questions alone.”

The value of diversity

The value of a diverse team is beyond dispute; It can help companies better combat against bias in their models, for instance. It’s also important to solving business problems which is presumably one reason you’re developing an AI strategy in the first place.

“It’s common knowledge that diversity of opinions is critical to all complex problem-solving,” says José Mendoza, senior Data Scientist and Managing Director here at Prophecy Labs. “Diversity is all about different life experiences, and professional background is a large part of most individuals’ life experience, which can add dimension to AI projects and provide new perspectives to finding innovative solutions.”

Mendoza also points out that building diverse teams – AI or otherwise – requires an active effort on the part of your company as part of recruiting and hiring practices. Sitting back and assuming diversity will find you is not a viable team-building strategy.

With that in mind, let’s look at a range of experts and roles – including non-technical roles – that can be valuable to an AI team.

Prophecy Labs, 2022

Data Team

1. Data Science Team Lead

A Data Scientist Team Lead responsibilities include managing the data science team, planning projects and building analytics models. Typically, Team leads have a strong problem-solving ability and a knack for statistical analysis. Their main objective will be to align data products with  business goals.

2. Data scientists

A Data Scientist is a professional who uses analytical, statistical, and programming skills to collect large data sets. They develop data-driven solutions explicitly tailored toward the needs of an organization.

3. Data engineers

A data engineers primary job is to prepare data for analytical or operational uses. These software engineers are typically responsible for building data pipelines to bring together information from different source systems.

Supporting Roles

1. Domain experts

You could also think of these as subject matter experts. Regardless of the term you use, it bears mentioning again their importance to your AI initiatives.

Developing an AI system requires a deep understanding of the domain within which the system will operate,” Experts in developing AI systems will rarely be experts in the actual domain of the system. Domain experts can provide critical insights that will make an AI system perform its best.

The type of domain expert needed depends on the problem to be solved, whether the desired insights are in the area of revenue generation, operational efficiency, or supply chain management, a domain expert is required to answer questions like [these]:”

  • What insights would be most valuable?
  • Can the data collected about the domain be trusted to be the basis for insights?
  • Do the derived insights make sense?

1. Business Intelligence Analyst

A Business Intelligence Analyst is a professional who works closely with stakeholders to identify goals, develop best practices for data collection, and analyse current processes to determine what can be improved to achieve their desired outcome.

2. Database Administrator

Database Administrators ensure that the databases run efficiently and securely. For example, they create or organize systems to store different data types, such as financial information and customer shipping records. They also make sure authorized users can access this information when needed.

3. DevOps / MLOps

An MLOps engineer essentially does the job of a DevOps engineer in the domain of machine learning. An MLOps engineer is in charge of everything that happens once the machine learning model is built. They put the model into production, test it to ensure it is working correctly, and optimize code for low latency

Organisational Structure

The data science team is best set up as a separate organizational unit which reports directly to the CEO (or CTO for example). This structure makes it easier for one team to serve the whole organization in a variety of projects. This is more efficient than having a distributed group of data scientists at separate business units. The data science team is supported by database administrators and MLOps engineers from the IT department and BI analysts from other departments. It is important for the data science team to participate in activities of the business units to get a better grasp of the typical problems that need to be solved and build good relations with these units.

The next step

While AI can solve some significant problems, it’s also virtually certain to create new challenges. This is fundamentally why the makeup of your team matters.

“People with different backgrounds and personalities tend to focus on different project details and constraints,” McGehee says. “This is useful because it raises the likelihood that all important details will be addressed, and provides a holistic approach to identifying solutions.”

Learn more about putting together top notch Data Science team, disover our free AI4Business course in our Courses section.

Andreas is driven by the interplay among strategy, relationship management and sales.

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