What are the top job profiles that an aspiring Data Scientist will be filling in at work? And do we really know the exact requirements for these roles?
Data Science is a multi-faceted discipline that combine several areas of technical expertise. Anyone acquiring overall expertise in this domain can be called a Data Scientist. But when it comes to hands-on business execution, specialisation is unavoidable due to the enormity of data-based activities in an organization. Depending on the specific data-tasks handled, several Data Science job roles have evolved across industries. They often appear to be overlapping in terms of skill-set and responsibility – but in reality, each of them has well-defined areas of action within the data framework.
Let us look at the ten top job profiles that Data Scientists currently fill in at the workplace. Although their names are extremely familiar, do we really know the exact requirements for each of these roles?
This is the fundamental job role in Data Science domain. As the name implies, the major focus in this job is on data analysis and reporting activities. A Data Analyst collects and organizes data, and then sifts out the necessary from the redundant. This cleaned up final data is the input on which they perform the required analysis and derive conclusions based on the findings. These findings could be revealing historical trends, or inferring future tendencies. Business decisions are made based on these findings. Visualizing the data patterns and presenting the results in the most effective communication format are necessary add-on requirements for the Data Analyst.
A Business Analyst is essentially a Data Analyst whose analytical activities are targeted towards the internal systems and processes of a business organization. The analysis is aimed at finding solutions to continually improve these business processes and design more effective ways-of-working. Business Analysts are expected to explore possibilities to streamline business operations, lower costs, and refine the decision-making process. Obviously, this will involve a clear knowledge of business processes and project management as well as expertise in software testing.
Business Intelligence Developer
A Business Intelligence (BI) Developer works on creating and maintaining BI interfaces and tools. Such BI solutions are used for data query, data visualization, data dashboard designing, and data reporting. Being focused on data-querying, BI Developer are usually adept in designing complex applications and statistical models via SQL, Python and R. This is a job role that combines the skills of Data Engineers, Data Analysts, and Software Developers.
Machine Learning Engineer
The Machine Learning (M/L) Engineer designs, deploys and maintains software and algorithms based on Artificial Intelligence (AI) technology. Here, the objective is to automate predictive models such that the system can use data inputs to self-learn “on-the-job”, and keep refining itself to produce more accurate predictions. It is the responsibility of the M/L Engineer to organize and analyse collected data and identify the best input to train and validate the machine learning model.
A Data Engineer essentially develops, implements and maintains the infrastructure that enables data cleaning, data preparation and manipulation. Data infrastructure is necessary to transform data into an analysable format – on which the Data Analysts can perform their duties. To create such an infrastructure, Data Engineers extract transform, and load the collected data – and continue to maintain and manipulate this data to always keep it updated for ready use.
It is evident from the job title that a Data Modeler designs maintains and refines data models. These models are part of overall database designing activities, and are deployed for database implementation. The Data Modeler works in tandem with the Data Administrators and Data Architects – looking for opportunities to improve overall data availability and database performance.
A Data Architect takes a high-level view regarding the architecture and infrastructure aspects of data management. This is a role that constantly focuses on the organizations specific business requirements and accordingly designs the end-to-end data management architecture of the company. The Data Architect is not only concerned with databases but also with the overall flow of data in the system – right from the point data enters the company till it leaves the system. That would span every data activity, like – data collection, data storage, data retrieval, data use, data modelling, and data security
A Database Administrator coordinates with Data Modelers and Data Architects to implement and maintain database solutions. However, while the modeler and architects deal with the theoretical logic of the database, the administrator handles the practical logistic and technical issues regarding deployment and maintenance. The Database Administrator ensures availability of and access to a database, monitors data backup and restore routines, manages overall database performance, and perform policing tasks to ensure data security and integrity.
This is a role that applies the principles of Data Science to marketing and sales data – with the aim is to solve associated business problems, like, field force sizing or marketing ROI. At a macro level, this job is similar to a general data scientist, but marketing data is the specialization here. A Marketing Scientist supports business decision-making by suitably interpreting data and identifying recognizable patterns in data to unearth latent trends in customer behaviour. Generally, this involves experiments devised through various data models to validate or reject any particular hypothesis.
Not all Data Science jobs directly deal with real-life data. There are some roles, like the Research Scientist, that is more concerned with the theoretical side of data research. This includes research on computing logic, user requirements from the data perspective, and business problem – spanning issues regarding clients, products, and solution features. There can be three specialties within this role: software, hardware, and robotics. Research Scientists get into the depths of computing challenges faced in the data analytics process and work on algorithms to solve the problems encountered. They develop the necessary coding languages, computing tools and software that all other data professionals use to play with data.