The 5 Must-Have Roles for Any Data Science Team


Building a data science product is similar to building a house. Let’s look at the five tasks and skills that the best data science teams hire for using this analogy.

A major financial services firm’s CEO was an outspoken advocate of advanced analytics. He made the decision to get his company started on the data science road.

What was the organization’s strategy for this journey? Of course, by enlisting the help of data scientists! They employed 1000 data scientists for an average annual salary of $250,000 each. Since data scientists are in short supply, the CEO was overjoyed at the accomplishment.

The company benefits were not there after several months and millions of dollars. Further analysis revealed that these so-called experts were not data scientists at all!

According to McKinsey, neither the CEO nor the human resources department at this company were aware of the data scientist position. They had the naive belief that a group of high-priced technological experts might transform the company into a data-driven one on their own.

Data scientists are often gifted in machine learning, but they struggle to identify the right business problems to solve. They are having difficulty scaling the algorithms in development. They often struggle miserably at converting data insights into a format that business users would understand.

Data science is a collaborative effort. If a team is serious about data-driven decision making, they must recruit five people.

Creating effective data science teams
Developing a data science solution is similar to constructing a house. It’s intuitively easy to grasp, but it’s a pain to put into practice. We’ll compare and contrast the five data science team roles with the five roles required to build your house.

  1. The “Architect” of Data Translation
    One of the most important positions of architecture is that of an architect. She learns about the desires of homeowners and assesses the viability of land use. She lays the groundwork for the rest of the construction team to build on by translating user needs into building sketches. She ensures that the house is usable, clean, and long-lasting and that it fulfills its promise.

A data translator, like an architect, is a company’s best hope for protecting its data science investment. The data translator deciphers a user’s business requirements and assists in the selection of the most appropriate projects for execution. She converts the specifications into an understandable format for the data science team. Her contribution to the project is ongoing, and she is critical to the creation of an actionable end product that consumers can use to make decisions.

Domain experts who are also proficient in business analysis are required as data translators. They are outstanding team leaders and communicators because they have a good understanding of data. They know how to use data resources like Microsoft Excel for a variety of purposes.

  1. The “Building Services Engineer” is a Data Scientist.
    Internal systems that render buildings functional and reliable are designed and created by a building services engineer. They build the heart of a home by overseeing systems such as HVAC, water, electricity, and control. These experts are now the brains behind self-regulating homes’ intelligent systems.

Similarly, the heart of a data science application is designed and created by a data scientist. He harnesses the power of data analytics to produce business-relevant, actionable insights. To embed knowledge and continuous learning abilities into solutions, he employs a variety of statistics and machine learning techniques.

Data scientists must be skilled in exploratory data processing, statistics, machine learning, and artificial intelligence techniques. They are also familiar with R and Python.

  1. The “Interior Designer” is the “Information Designer.”
    To build a practical and aesthetically pleasing interior, an interior designer collaborates with the architect and engineers. She sketches out rough ideas for how the room will be used. She creates detailed plans and determines the type of building materials to use by iterating on them.

The data science solution is made practical and appealing to use by an information designer. She creates mockups and comprehensive concept iterations after establishing the information architecture. She makes data insights digestible by selecting the appropriate maps, interactivity, and graphic design. With data, she is a master storyteller.

These information design experts are also proficient in fields of interaction and graphic design. They use modelling software like Sketch and Adobe Illustrator, as well as exploratory visualisation software like PowerBI.

  1. The “Civil Engineer” is a Machine Learning Engineer.
    The concept is brought to life by a Civil Engineer.
    The concept drawings are brought to life by a Civil Engineer. He examines and reviews all designs to ensure that they are carried out as intended. He keeps track of procedures, keeps construction records, and ensures that industry guidelines are followed.

The working application is designed by a Machine Learning (ML) Engineer. He links to data sources, bundles machine learning modules, and interacts with all other systems on the backend. He brings the front-end to life with a user interface that is both usable and effective. He keeps records, registers the code, and follows software engineering standards.

DevOps experts with good backend/frontend coding skills are required as ML Engineers. They know how to use languages like Python, JavaScript, and SQL, as well as cloud platforms like Google Cloud.

  1. The “Construction Manager” is the Data Science Manager.
    A Construction Manager supervises the project and ensures that all promises made to the homeowners are kept. She is in charge of plans, efficiency, and finances. Her duty is to make sure that all positions not only complete their tasks but also work well together. She manages workplace problems, ensures workplace safety, and maintains morale.

A Data Science Manager, on the other hand, is the shepherd of a data science team, putting all of the positions together and inspiring them to do their best work. She honours her client obligations and keeps all lines of communication open. She ensures that high-quality products are delivered on time. More specifically, she is in charge of change management and business user acceptance of the solution.

Data Science Administrators are outstanding project managers with a strong understanding of change management. They’re well-versed in market research and the methods for framing data science solutions. Microsoft Project is one of the project management tools they use.

Creating a data science team that has a positive effect on the company
We’ve looked at the roles that are required to build your house. What about the building materials you’ll need, such as bricks, steel, or wood? Data, in our analogy, is the raw material used to create every data science product. Before you can act on data, you must first compile, curate, transform, and store it.

These operations are handled by the field of computer engineering and positions such as data engineer. In this article, we’ve focused on data science, a discipline that aids in the extraction of business value once your data is accessible. We’ve seen the areas of expertise needed for each of the five positions. They must also possess certain non-negotiable, essential abilities.

To understand the market challenges they are solving, everybody needs a basic domain orientation. They should be data literate, meaning they should be able to comprehend, interpret, and communicate data. Finally, they should all have outstanding communication and presentation skills in order to communicate with the rest of the team as well as their clients.

Machine learning and artificial intelligence are hot topics these days. All of this excitement has resulted in an overabundance of emphasis on the sexiest job title of the century: data scientist! To make data science work for your business, you’ll need cross-disciplinary skills.

To build a meaningful, consumable, and actionable business solution, every team requires each of these 5 data science positions.