10 Things to Consider While Building your Right Data Science Team

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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Enterprises these days no longer follow outdated business processes.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. Ensuring data quality helps an organization make better decisions.

Many of you would have already heard of data scientists. A core data scientist is supposed to know everything and manage it all. But that is hardly possible. Even though the same 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 analysis.

What is Data Science?

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

In computer science, data analytics is the process of analyzing raw data to make conclusions about it.

data science

A data science company offers multiple services related to data analytics, data infrastructure, data strategy, 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 advanced 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. A global digital framework can introduce governance, the social environment, business, and technology. In such instances, hiring the services of a data science company is a better option.

How Data Scientists are different from Data Architects?

A data architect has an evolving role, so there is no industry-standard certification program. As data engineers, data scientist experts, or solutions architects, individuals typically gain experience in data design, data management, and data storage work as they work their way up to the role of a data architect.

The Importance of Building the Right Data Science Team

What if you want to build a team for data science projects? At what stage should you introduce data science into your enterprise? Mostly, the decision has the highest impact at early stages only.

How important is it to build the right team?

Introducing data science roles 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.
Many companies expect data analysts to be able to convert alienating numbers in order to provide tangible insights.

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 types of data science teams 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 who require data analysts with market-tested skills.

  • 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. It totally depends on the business objectives to opt for a decentralized model.
  • Centralized: These are data science teams that work 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 engineering team can be hired by different departments to work on specific projects. This structure works from SMEs where the management cannot allocate many external resources to the teams.
  • Democratic: This allows you to combine and integrate the simple or specialized 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 types.

data 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 (further reporting by multiple teams). In the centralized model, the team reports to the head of data analytics. 

Data Science Skills to Consider While Building the Team

It is not casual to build data science teams. You will need to consider a lot of factors in choosing every member of the team and assigning them their respective team roles. 

The method and viewpoints we use are different based on the skill sets and expertise of the individuals in our team. Moreover, data science is a team sport, so accurate teamwork is essential!

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 models across the business. The responsibility of managing the team and ensuring that they are delivering the required data insights lies with the team leader or the Chief Analytics Officer/Chief Data Officer.

Eventually, they make their way to being business leaders.

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

Apart from the command over how data science functions effectively, the individuals should preferably be well versed with business analysis too. Harvard Business Review provides exceptional content on new ideas and how leadership should be at the time when business problems emerge.

2. Project Portfolio

The data scientists, machine learning engineers, and other analysts (software engineers in rare scenarios) should have worked on at least a handful of big data and data science field projects. They should have successfully built models to gather and collect 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. A Data scientist with diverse portfolios should be a preferred choice of addition to a team of successful data visualization engineers.

Data preparation in a most interactive way implies converting business expectations into useful insights with the data team.

3. Diversity in Academics

For a data 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 ML Engineer’s job might include specific tasks on recommendation engines.

A business analyst would come from a statistics and mathematics background.

You will need strong programming skills in certain 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.
Strong programming language is a must.

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. They are much familiar with model training and have a deeper understanding.

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 projects and the majority of complex data science tasks. 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 engineering teams indeed work on data without knowing what they are arriving at or how it will help the business (if it will even help).

Additionally, there are various aspects on an operational level that could address a business problem in day to day interpretation activities. Project managers along with the whole team have different responsibilities and work closely so the approach entails the best way for data related use cases.

But the right data science team is the one where the data analyst role knows the purpose of his/her job. Knowing what the management is looking for will help the team in analyzing the data sources 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 one 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.

As an example, a technical aspect can be “Central tendency” – a single value that identifies the central point within a set of data in an attempt to describe that set of data as a whole.

Domain expertise is a must, at least for the team leader, though we recommend initiating a hiring process for a team of domain experts. This will ensure that the success rate of the projects increases and the data analysis and multiple ML models tailored 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 manager or the entire team has the required technical skills as well.

For example, the group of data science experts must have hands on experience with data related functions used in Python which might play an important role in complex projects.

Data visualization is another part of the process. Your data scientist should be a certified expert who can work with data visualization software like Tableau or Power BI. Skills to work on AWS, Azure, GCP is 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.
Along with programming skills, data visualization skills (presentation skills) are important too.

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.

Another thing could be implementing “The Team Data Science Process (TDSP)”. It is an adaptive data science approach for creating predictive analytics applications and intelligent programs.

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 successful data science teams’ 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.

From working on new algorithms to solving data focused personalisation use cases. An engineer builds data pipelines and comes up with relevant questions that further add to a custom built solution. Like if someone has prior experience to build recommendation systems, then that person is contributing to a big picture whether in a single category or more than two types.

Right people often fit into many roles in managing a data house.

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.

1 Comment

  1. I’m happy to find numerous useful information here in the post, we need to develop more strategies in this regard, thanks for sharing.

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