Creating a Data Science Team: Four Things to Consider

data science team

When you decide that your organization must start leveraging data, it can be a highly challenging task at first. Many businesses, especially startups and new ones, are unaware of what goes into becoming a data-driven entity. The first step is to put together a data science team. 

Building a data science team is not different from building a house. The location and the architecture are the primary considerations. For data science, the techies come in first. The programmers and others come in later. 

Many organizations find it easy to hire talented data scientists. The challenge lies in harnessing their capabilities for the benefit of the organization. 

Supporting and eliciting the best out of data science teams demand a specific set of practices. These include identifying problems, setting up a system of measurement to assess success, and analyzing the results. These steps require more clear business acumen and less technical knowledge. 

Data science teams can bring immense value to any business. But they must get proper guidance and support.

These steps can help data science teams realize their full potential and bring more benefits to your organization.

Identify the Problem Areas Accurately for the Team

Point out the problem areas of your business with a high level of accuracy to the data science team. Data scientists will begin the analysis by building models based on available input. They may not ask questions of your senior executives, especially if they are new to the industry. You must make sure the team focuses on the right problem. 

To boost the chances of identifying the right problem, check competitors and other early adopters of data science. What are they doing? Do not copy their methods of solving problems as there are multiple approaches possible to the same situation. Create a uniquely innovative approach to finding the right solutions.

Ascertain a Clear Evaluation Metric

As mentioned, there are many paths to problem-solving. Data science teams build multiple models and zero in on what they believe is the best. To make this choice, they are guided by a metric. This metric ranks the models and chooses the most appropriate one. 

Business leaders can utilize their expertise and acumen to determine the metric. However, that’s easier said than done. There is no single foolproof metric in a complex business situation. You will find many relevant metrics, and they often conflict with one another. 

For instance, you need a model to help the sales team identify the most potential customers. You must find the metric that can provide the right solution.

The error rate is a reasonable metric, but it has two elements. It offers a rate of false negatives in one instance. The prospects are considered not worth contacting as they are predicted to be a loss. In the second instance, the prospects are believed to be a win for the company. But often, they turn out to be a loss. It is a case of false positives. 

A model with the lowest error rate may include false positives and false negatives. But that may not be ideal for your business. These errors can have very different impacts. A model with a higher overall error is preferable. The false positives and false negatives balance and work better for your business. 

Your data science team will provide input on choosing the right metric for your industry. This decision is based on their past evaluations of similar programs. Your data science team can even create a custom metric if the existing models are not a good match. 

Create a Logical Baseline

The next step is creating a logical baseline when the problem is identified, and an evaluation metric is identified. It simply means how your team would solve the problem without any data science knowledge. 

Example: Your data science team is working on developing a unique recommendation algorithm for your eCommerce website. The baseline will be about understanding what products the visitors will look at. A logical extension would be recommending products from those categories. Indeed, look at the latest data science trends for ideas. 

The baseline will ensure the team gets working on the data and evaluation pipeline. The process will help uncover any issues and identify tactical obstacles by calculating the evaluation metric. You will know what to expect from the project. Ask any experienced practitioner, and they will agree that logical baselines are crucial for any business. 

Scrutinize the Outcome Intensely

The results of the evaluation need intense scrutiny. This is crucial to make sure the benefits are real. It will also ensure there are no inadvertent negative consequences. The most basic move? Check if the results are calculated on data not used to build the models.

Also, check for any adverse side effects. A model takes your performance to a higher level with a particular metric. But it may be at the cost of other vital metrics. 

For example, your business may focus on boosting revenue per visitor with a powerful recommendation algorithm. Revenue per visitor takes into account the conversion rate and revenue per conversion.

Your algorithm may improve revenue per conversion, but what if it pulls down the conversion rate? It can affect your strategic business objective of converting more visitors into customers. The end result? Diminished growth in the future.

A tradeoff between one metric and another doesn’t make sound business sense if it goes against your larger business goals.


Many organizations want to build data science teams, but not all of them are successful. Certainly, by applying the strategies mentioned here, you can improve your chances of success. It is important to note that the end goal is accomplishing the mission of your business. It cannot be achieved just by leveraging technical skills. You must also take human connections into account while building a data science team.