Dealing with big data is mostly new territory for lots of organizations. Many just employ individuals that have Data Analyst Qualifications and hope that they can solve all the organizations' problems both past and future. As you already guessed, this strategy doesn’t always work out quite well and often ends in a disaster. Big data and data science is something that needs to be handled by a group of experts. A team. As data science is a team sport, you need to hire people who not only have Data Analyst Qualifications but also people who work better in a team-setting, no matter what their individual capabilities be. It takes a large group of people, all having worked under different settings, coming and working together, to solve practical data science projects and build big data strategies. Some of the key team members that a data science team should have are:
The Data Scientists
The data scientist is the one who drives engines for innovations in the projects. The data scientists start by creating various Machine Learning-based tools or processes within the company, such as recommendation engines or automated lead scoring system and of course systems that will help process big data. The data scientist should also be able to perform statistical analysis. They are usually the leader of the team
Functions of a Data Scientist: So a data scientist might be somebody who would go and pull data out of a database, analyze it, perform experiments on it, visualize it and communicate those results to the data science manager, and to other people in the organization who will then move things forward. Often, a data scientist will pass on the implementation of any machine learning algorithm for prediction algorithm they develop, to the data engineer, who will then make sure that the program can run at scale.
The Project Manager
The project manager is the one that makes sure everyone sticks to a timeline. The job of the project manager is to plan, budget, oversee and document all aspects of the specific project they are working on. Project managers may work closely with upper management to make sure that the scope and direction of each project are on schedule, as well as other departments for support.
The Data Engineers
The data engineers are the ones who perform and develop the infrastructure. Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance, and testing of architectures, such as databases and large-scale processing systems.
Functions of the Data Engineer: As already mentioned a data engineer is a person who would have to deal with setting up the required infrastructure environment. They are also responsible for converting theoretical algorithms and ideas into running code and applications. A data engineer might construct a database, or pull data out of that database for the team to analyze. The data engineer might also have to convert ideas into production level machine learning products and convert them into a client-server model, so that they can be applied to a huge database of observations, or even run in real time, so that the product uses data, to get smarter with time.
The Data Science Manager
Is the name implies, the data science manager is in charge of managing the team and keeping it in lace and running efficiently. Team member usually has other tasks to handle and it can be quite easy to forget to communicate with other team members. This is where the data science manager comes in. The data science manager makes sure that everybody interacts with each other and that things keep moving. They also recruit and build the data science team, interact with upper management in the organization and collaborators that are at their same level across the organization, to make sure that they get all the information across. The data science manager shares the findings of the data science team to other people, and their capabilities and encourages people to bring their problems to the team. They handle communication, public relation, diplomacy between the team and other departments of the organization. They are usually the face of the data science team.
Functions of The Data Science Manager
- They balance technical nuances across domains of data, big data, statistics, machine learning, artificial intelligence and any other software that the team needs and uses.
- They work to earn the respect and trust of the technical team and the whole organization by contributing at big picture decision making and providing feedback on decisions made.
- They add structure to the team. For example workflow, an agile process with feedback loop and code reviews, code repository, and documentation. This structure absorbs shocks and removes barriers, identifies disconnects and builds consensus, facilitates a smooth work environment, manages workload, sets the pace and maintains quality
- They take ownership and control of key workflow areas such as data acquisition, data quality, prioritizing which aspects are most important, presentation of results.
- They use their knowledge, skill, and expertise to launch data science solutions for real-world applications
- Lastly, they plan, manage, and coordinate the business process changes and production level code such as information technology (IT). This helps the team be more efficient and able to carry out their plans more accurately and timely. This last function is carried out hand in hand with the project manager. The data science manager and the project manager are two members of the team that works more together and hands in hand to keep the team dynamics running smoothly.