1. SEJ
  2.  ⋅ 
  3. SEO

3 Types of Data Science SEO Teams and How They Work

The team is the most important element in successful data science for SEO. Here are 3 main types of teams and how they work.

When it comes to successful data science for SEO, nothing is more important than having the right team in place.

Challenges in obtaining and ensuring the consistency of the data, as well as in your choice of machine learning models and in the associated analyses, all benefit from having team members with different skill sets collaborating to solve them.

This article presents the three main types of teams, who is on them, and how they work.

Let’s open the floor with that loneliest of data science SEO professionals — the team of one.

1. The Solitary Data Science SEO Pro

The one-person team is often the reality in small and large structures alike. There are plenty of versatile people out there who can manage both the SEO and the data functions on their own.

The lone data science SEO professional can generally be described as an SEO expert who has decided to take advanced courses in computer science to focus on a more technical side of SEO.

They have mastered at least one programming language (such as R or Python) and use machine learning algorithms.

They are closely following Google updates like Rankbrain, BERT, and MUM, as Google has been injecting increasingly more machine learning and AI into its algorithms.

These pros must be skilled in the automation of SEO processes to scale their efforts. This might include:

  • Automatic indexing of new URLs in Bing.
  • Creation of sitemaps with the new URLs for Google.
  • Text generation with GPT models.
  • Anomaly detection in all SEO reports.
  • Prediction of long-tail traffic.

In my organization, we share these SEO use cases in the form of a Jupyter Notebook. However, it is easy to automate them using Papermill or DeepNote (which now offers an automatic mode to launch Jupyter Notebooks regularly) in order to run them daily.

If you want to mix it up and enhance your professional value, there are excellent training courses for SEO enthusiasts to learn data science — and conversely, for data scientists to learn SEO, as well.

The only limit is your motivation to learn new things.

Some prefer working alone; after all, it eliminates any of the bureaucracy or politics you might (but don’t necessarily have to) find in larger teams.

But as the French proverb goes: “Alone we go faster; together we go further.”

Even if projects are completed quickly, they may end up as successful as they could have had there been a wider range of skills and experience at the table.

Now, let’s leave the solitary SEO and move on to teams of two people.

2. The Data Science SEO MVT (Minimum Viable Team)

You may already know MVP as a Minimum Viable Product. This format is widely used in agile methods where the project starts with a prototype that evolves in one- to three-week iterations.

The MVT is the equivalent for a team. This team structure can help minimize the risks and costs of the project even while bringing more diverse perspectives to the table.

It consists of creating a team with only two members with complementary skill sets — often an SEO expert who also understands the mechanisms of machine learning, and a developer who tests ideas.

The team is formed for a limited period of time; typically about 6 weeks.

If we take content categorization for an ecommerce site, for example, often one person will test a method and implement the most efficient one.

However, an MVT could perform more complex tests with several models simultaneously — keeping the categorization that comes up the most often and adding image categorization, for example.

This can be done automatically with all existing templates. The current technology makes it possible to reach 95% of correct results, beyond which point the granularity of the results comes into play. can help you stay up to date with the current state of technology in each field (such as text generation), and will most importantly provide the source code.

GPT-3 from OpenAI, for example, can be used for prescriptive SEO to recommend actions for text summarization, text generation, and image generation, all with impressive quality.

3. The Data Science SEO Task Force

Come back in time with me for a moment and let’s take a look at one of the best collaborations of all time: The A-Team.

Everyone on this iconic team had a specific role and as a result, they succeeded brilliantly in each of their collective missions.

Unfortunately, there were no episodes on SEO. But what might your data science SEO task force look like?

You will surely need an SEO expert working closely with a data scientist and a developer. Together, this team will run the project, prepare the data, and use the machine learning algorithms.

The SEO expert is best positioned to double as a project manager and handle communication with management and external stakeholders. (In larger companies, there may be dedicated roles for the team’s manager and project leader.)

Here are several examples of projects that this type of team might be responsible for:

  • Setting up an enterprise data warehouse (an out-of-the-box data warehouse that merges business, market share-of-voice, technical, and content data).
  • Identification and resolution of “zombie” pages.
  • Detection of new queries.
  • Forecasting of traffic/profits following certain actions.

Data SEO Compliance

Of course, teams need tools to maximize their efforts. This brings us to the idea of data SEO-compliant software.

I believe there are three principles to adhere to carefully here in order to avoid using black-box tools that give you results without explaining their methodologies and algorithms.

1. Access to documentation that clearly explains the algorithms and parameters of the machine learning model.

2. The ability to reproduce the results yourself on a separate dataset to validate the methodology. This doesn’t mean copying software: all the challenges are in the performance, security, reliability, and industrialization of machine learning models, not in the model or the methodology itself.

3. The tool must have followed a scientific approach by communicating the context, the objectives, the methods tested, and the final results.

Data SEO is a scientific approach to optimizing for search that relies on data analysis and the use of data science to make decisions.

Whatever your budget, it is possible to implement data science methods. The current trend is that concepts used by data scientists are becoming increasingly accessible to anyone interested in the field.

It is now up to you to take ownership of your own data science projects with the right skills and the teams. To your data science SEO success!

More Resources:

Vincent Terrasi Co-Founder / Expert Innovation / R&D / Data Science / Chief Product Officer at DnG

Vincent Terrasi is the Co-founder and CTO at Draft&Goal. With more than 15 year’s experience, Vincent has become an expert ...

3 Types of Data Science SEO Teams and How They Work

Subscribe To Our Newsletter.

Conquer your day with daily search marketing news.