The worlds of business and science often overlap bringing new and innovative solutions to the forefront. Today this overlap is even more imperative for the future as we are faced with new challenges and complexities. With the amount of global data expected to vault to 175 zettabytes from 47 zettabytes by 2022, having a data science team available is critical for interpretation.
A data scientist can help transform that mass of data into usable intelligence. If you think of them as a magician of data, you would not be too far from the truth. Data scientists use new technologies like Machine Learning (ML) and Natural Language Processing (NLP) for their work as well as older mathematical principles like statistics and an analytic approach to help organizations solve problems.
The field of data science is not new. It has however been revitalized and revolutionized in recent years due to the advances in AI and ML. Successful data scientists have many different skills available. From a computing point of view, they are expected to know how to program and should be able to design new algorithms and new data science apps.
But a data scientist needs more than just computing skills. A data scientist needs to understand business and should have the ability to describe data science findings eloquently. This could include the creation of different visualization techniques to share information as well as an ability to narrate informative stories about their findings.
In addition to the business skills, a data science team should also have a very strong mathematical bent. This will enable them to build models using statistics and even find patterns in data. Some examples of this include an ability to predict the stock market or the creation of a recommendation engine.
However, in some cases, data scientists are presented with data without a particular business question or objective in mind. In these instances, it is expected that the data scientist would explore the data, coming up with relevant questions and answers that the business could make use of.
This can be difficult, but those data scientists with a knowledge of different techniques like ML engineering and Big Data Processing can successfully navigate the challenges. For these data scientists knowledge of how to manipulate data with the latest cutting edge technologies is a useful prerequisite.
Data science as a field is new – it has only really come to the fore in the past several years, but in that time it has become a critical area of study for many around the world. Considered by the Harvard Business Review as the sexiest job of the decade it is one of the fastest-growing jobs on LinkedIn in terms of opportunities.
The amount of data in the world now is only going to increase in the years ahead which will further propel the popularity of data science and data science team roles. Among many other responsibilities, a data science team is responsible for the delivery of complex projects. In these projects, various disciplines and skills are needed and there is often a confluence between software and data engineering as well as data analysis.
Within the team, many different specialties assist including business analysts, data engineers and architects, and a data analyst. A data scientist helps interpret the data so that the information makes sense but understanding the roles of everyone within the data science team is crucial.
When building a data science team it is important to understand the different roles and responsibilities that individuals fill. Within most data science teams, the following four roles need to be filled:
Each of these positions has different responsibilities as described in greater detail below.
The group manager plays a significant role when it comes to data science teams in firms. In many businesses, data science units are comprised of multiple teams each with different goals. The group manager is responsible for the creation of a collaborative group environment and works on the Team Data Science Process (TDSP).
As an example of their responsibilities, the group manager would perform the following functions with Microsoft Azure to launch a project.
The team lead picks up from the group manager and continues the work to create a collaborative team environment using the standards provided in the Team Data Science Process (TDSP). The team lead and group manager could be the same person depending on the size of the team. Their primary function is the leadership of a team of data scientists.
The team lead looks after the following tasks in the TDSP to ensure project success.
As per the Team Data Science Process (TDSP), the project lead is responsible for the day-to-day activities of the data science project.
The project lead will create a project repository and enable file storage to store the team’s information and data. They will add project members to the project and enable the required permissions.
The individual contributor on the data science team is often the data scientist themself.
They are responsible for cloning the project repository and the actual execution of the project.
When thinking about data science team roles, there are two things to consider. There are two types of data scientists. Type A data scientists look after analysis. These are the data scientists that work with data and look after data cleaning, modeling, and forecasting.
Type B data scientists are strong software programmers with good engineering skills. Type B scientists are responsible for building and as such, they build recommendation systems as well as use cases.
Within any organization focused on data science, you can expect to have the following data science roles in place.
When building a data science team, this role is a critical one. The Chief data officer (CDO) looks after lots of different data-related functions. This includes areas of focus like data quality and data management as well as the creation of the overall data strategy. The Chief data office and chief analytics officer (CAO) are both unique roles, however, based on the organization, they could be filled by the same individual.
A business analyst has the same role as a Chief analytics officer but their focus is more tactical versus strategic. They use data to determine project requirements and deliver recommendations and reports to stakeholders.
Data architects and engineers work together to build a solution. The architect visualizes the requirements for the framework, while the engineer builds the digital framework.
The data analyst looks at data that has been collected and makes sure that it is useful and comprehensive. The analyst is responsible for interpreting the data so many businesses look for an analyst with strong visualization skills.
Data scientists fulfill a dual role. They have the skills needed to solve complex technical issues, but they also have a natural curiosity so they know what questions need to be asked. Data scientists can develop ML models, but they require access to copious amounts of data. Having access to data helps the data scientist detect patterns and relationships helping them build theories.
Machine learning engineers are distinct from data scientists. Machine learning engineers combine software engineering skills with machine modeling abilities. They determine which model to use and what data is required for the model.
This role is somewhat unique as it’s not a requirement for all data science teams. However, with specialized data science models, the role of the data visualization engineer is crucial. Successful data visualization engineers need to have a solid foundation of UI skills to help create unique data visualization elements for stakeholders. He also defines which metrics and charts would be the most beneficial for business.
The Team Data Science Process (TDSP) is a methodology focused on delivering intelligent applications and predictive analytics solutions in an efficient manner. The TDSP helps define the best way for teams to collaborate and work together. It includes structures from organizations like Microsoft and other eminent industry leaders that help define the best practices for data science projects. The goal of the TDSP is to help firms achieve the greatest benefit from their analytics programs.
One of the key components of the TDSP includes a data science lifecycle. The TDSP defines a standard project structure along with recommended resources and infrastructure for data science initiatives along with tools and utilities needed for project execution.
Building a data science team does not need to be a complicated undertaking if done with care and forethought. Some simple secrets to succeeding include looking for internal talent within your organization. By using in-house resources you can quickly ramp up on certain elements while looking externally for expertise that might be lacking.
When building your team don’t try to hire based simply on the title. Instead, look at the roles available and how many of these a single data specialist can handle. Due to the scarcity of talent, finding external resources can be costly, so an outsourcer with the capabilities in-house might be a better option.
Within the organization ensure that the data science team is quickly integrated into the corporate culture and understands the objectives of the organization. Also, look towards building a solid team environment and culture before slotting new individuals into it. Consider the knowledge and skills of your product manager and ensure that they understand the key differences between software and data products.
Data benefits all levels of an organization from the C-suite to the frontline manager. Companies that struggle to understand the data they have access to are going to struggle to compete. The role of the data science team and the data scientist is critical within many organizations today.
However, building a data science team from scratch is not always a cost-effective solution. In many cases using an outsourcer like NIX with the skills, in-house is a better option. NIX can work with you to understand your business objectives and build a solution that will help you achieve success. Contact us to find out how we can help.
An AI Solutions Consultant with more than 10 years of experience in business consulting for the software development industry. He always follows tech trends and applies the most efficient ones in the software production process. Finding himself in the Data Science world, Evgeniy realized that this is exactly where the cutting-edge AI solutions are being adopted and optimized for business issues solving. In his work, he mostly focuses on the process of business automation and software products development, business analysis and consulting.
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