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10 Things to Consider While Building your Right Data Science Team

Enterprises these days no longer follow outdated business processes. The focus is more on adopting the latest technology and relying on new software and tools to increase productivity and ROI. But working with advanced systems means hiring experts who have experience in the said field.

Data is a major part of every business. Be it artificial intelligence, machine learning, natural language processing, or business intelligence, these technologies work with vast amounts of data. Organizations need employees who can work with data and the latest software to derive insights and generate reports.

Many of you would have already heard of data scientists. A data scientist is supposed to know everything and manage it all. But that is hardly possible. Even though the person should have a varied skill set, a data scientist alone will not be enough. What enterprises need is a fully equipped data science team to work on big data.

What is Data Science?

Before we talk in detail about why and how you should build a data science team, let us first see what it actually is. The simplest definition is-

data science

data science company offers multiple services related to data analytics, big data, deep learning, machine learning, artificial intelligence, and much more. The technologies and services are interlinked with the teams working on different aspects of data science and analytics.

Not every business has the suitable infrastructure to build a data science team. While some find it easy, others have to make a lot of effort. In such instances, hiring the services of a data science company is a better option.

The Importance of Building the Right Data Science Team

What if you want to build a data science team? At what stage should you introduce data science into your enterprise? How important is it to build the right team?

Introducing data science into your business processes requires a lot of planning. You will need to be sure that you have enough budget to invest in systems, people, and processes. You also need to be assured that your existing employees will welcome the changes and embrace them. If your employees do not value the insights offered by data scientists, the purpose will be lost.

The following are some reasons you should invest in building data science for your business.

  • Empower the employees and management
  • Recommending future actions based on insights derived from past and real-time data
  • Identifying growth opportunities in the market
  • Helping in better decision making based on data-driven reports
  • Assisting the employees to adopt industry best practices
  • Analyzing and evaluating the decisions made by the management
  • Identifying the target audiences
  • Identifying customers’ issues and finding solutions
  • Helping recruit the right talent for the business

data science team

Data Science Team Roles

A data science team has multiple experts, each dealing with different aspects of the field. The roles and responsibilities of the team members depend on their experience in domain expertise, technical knowledge, and quantitative skills.

  • Team Leader- Chief Analytics Officer or Chief Data Officer 
  • Data Strategist
  • Data Scientist 
  • Data Engineers and Architects
  • Data Analysts
  • Machine Learning Engineer
  • Business Analyst 
  • Data Journalists
  • Data Visualization Engineer

The actual team positions might differ, depending on the type of data science team an enterprise wants to build and how much it can invest into it.

How to Structure the Data Science Team?

The data science team structure can further be classified. You will first need to decide the type of team you want to build in your organization and then hire the right kind of experts.

  • Decentralized: Works the best for short-term, initial data science integration activities and SMEs that don’t want to become a full-fledged data-driven business.
  • Centralized: This is a data science team that works on multiple projects scattered in different departments throughout the enterprise. This structure works well for enterprises focusing on long-term growth and development.
  • Functional: One team works with one department like the marketing or the logistics. The focus area is limited to that department. This structure is best suited for startups where there is no need to analyze every single piece of information.
  • Center of Excellence (CoE): This is similar to the centralized structure but with a separate unit for data scientists. It is known as one of the most balanced structures since there is a higher level of coordination between the teams.
  • Consulting: This is similar to having a data analytics company within the enterprise. The data science team can be hired by different departments to work on specific projects. This structure works from SMEs where the management cannot allocate many resources to the teams.
  • Democratic: This allows you to combine and integrate the data science model with other systems in the enterprise. Employees have access to data science systems and can make changes to them. This works when businesses focus on building data science infrastructure for the enterprise.
  • Federated: This is similar to employing a SWOT team in the organization. The federated structure is a combination of decentralized and CoE science process

So does this make you wonder who should the data science team report to? Well, the answer lies in the structure of the team chosen by the enterprise. In large enterprises, the team reports to the COO, CTO, CPO, or CFO. In the centralized model, the team reports to the head of data analytics. 

Factors to Consider While Building the Right Data Science Team

We know that building a data science team is not a casual task. You will need to consider a lot of factors in choosing every member of the team and assigning them their respective team roles.

How to build a data science team from scratch?

  • Hire experts and specialists 
  • The team should be autonomous 
  • Get the expectations right
  • Don’t do things half-way 
  • First target the low-hanging fruits

Take a look at the factors mentioned below.

1. Leadership Skills

One of the main reasons for having a data science team in your organization is to load balance the machine learning model across the business. The responsibility of managing the team and ensuring that they are delivering the required insights lies with the team leader or the Chief Analytics Officer/ Chief Data Officer.

Though most data scientists and analysts are exceptionally talented at their work, we need to emphasize their leadership skills, too, when choosing the team head. Someone who has strong domain expertise, problem-solving abilities, reasoning, and decision-making skills and someone who can communicate with employees across the levels will be suitable for the role.

2. Project Portfolio

The data scientists, machine learning engineers, and other analysts should have worked on at least a handful of data science and big data projects. They should have successfully built models to gather data, process it, and derive accurate insights.

Experts who have worked with predictive analytical models that lean towards deep learning, NLP, and sentiment analysis will bring good experience to the team. Data scientists and analysts with diverse portfolios should be a preferred choice of addition to the data science team.

3. Diversity in Academics

For a team to be well-rounded, you will need to hire people with varied academic qualifications related to the field. For example, a machine learning engineer comes from an engineering background. A business analyst would come from a statistics and mathematics background.

You will need programming with expertise in coding languages. A data journalist is someone who works with programming languages, data visualization tools, and database management software solutions. The more diverse the team’s overall academic qualification, the more the efficiency of the team and the better the results.

4. In-house Talent

When building data science capability for your enterprise, start by looking within the enterprise. Before you hire external talent, make sure you have searched for options from within the business. Some of your employees might have been doing certification courses or working on ML models as a personal project.

You can also list out the potential employees who can be trained to work with external experts and become a part of the data science team. This will make it easy to adopt the new processes in the enterprise. And most importantly, it will give you an exact idea of the talent gap you are facing.

5. Business Awareness

Though the task of the data science team is to gather insights from a large amount of data, it doesn’t mean they do not have to be aware of the business. Some data science teams indeed work on data without knowing what they are arriving at or how it will help the business (if it will even help).

But the right data science team is the one where the team members know the purpose of their job. Knowing what the management is looking for will help the team in analyzing the data with increased effectiveness. The models built by them will also be accurate and reliable.

6. Domain Knowledge

Domain knowledge is a must for any job. The person you hire should have expertise in the field they work. They should have command over the topics, tools, techniques, processes, and systems that belong to their domain.

Domain expertise is a must, at least for the team leader, though we recommend hiring a team of domain experts. This will ensure that the success rate of the projects increases and the data analytics models built by the team will have little or no flaws. The team will also have the capabilities to scale and upgrade the models over time.

7. Technical Abilities

Database management, programming, computing, continuous integration of tools and systems, working on the cloud platforms, etc., are possible only when your data science team has the required technical abilities as well.

Data visualization is another part of the process. Your data scientists and analysts should be certified experts who can work with data visualization software like Tableau or Power BI. Skills to work on AWS, Azure, GCP are just as essential as knowing JavaScript, Python, R, etc. Of course, having these in the resume will not be enough. The professionals should prove their abilities through the number of successful projects they have completed.

8. Attention to Detail

This one is usually listed under personal skills, along with domain knowledge, communication, data intuition, passing for learning, and intellectual curiosity. Working on big data, machine learning, NLP, etc., requires extra alertness from the team members to ensure that there are no minor errors that could cause havoc in the entire model. It is almost always those tiny details we miss that end up causing maximum trouble.

9. Communication Skills

A lot of teams do not achieve their targets because of miscommunication and lack of proper collaboration between the team members. This led to a few multinational companies coming up with team-building exercises as a part of data science team best practices.

From organizing outings to facilitating multi-level communication within the organization, businesses are focusing on building teams where the team members can express their views, be heard, and learn from each other.

10. Passion for Work

How can you build the right data science if the professionals don’t have the passion to build new models, discover insights, and provide valuable reports for the enterprise? You also need to make sure that the team is always motivated to deliver its best.

data science team


Building the right kind of data science team for your business is a time-consuming and cost-intensive process. You will need to factor in multiple elements before you hire each team member. An easier alternative would be to contact a data science company in India and hire their expertise.

Data science and data analytics firms think of the future when they build teams of experts to assist various SMEs and organizations from around the world. That’s how they provide the best data analytics services in the market. Hiring the services of data analytics firms can give your business the required power to forge ahead of your competitors.

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