To handle 2.5 quintillion bytes of data produced every day, enterprises need professionals who can treat, analyse and organise this data to provide valuable business insights, for intelligent actions. A data scientist dons many hats in his/her workplace. Not only they are responsible for business analytics, they are also involved in developing software platforms and building data products, along with being experts into data visualizations and machine learning algorithms.

Much has been spoken about a data scientist being is the sexiest job title of the 21st century and data science as the most promising field. Building grounds on what that is already been written and said, Analytics Insights compiles the list of the top Data Science jobs for the month of August-

• Data Scientist

Average Salary: US$140,000

Data Scientists analyse the source of data with an effort to clean, and organize it for companies. Data scientists need to analyse large amounts of complex raw and processed information unearthing patterns that will benefit an organization that drive strategic business decisions. Compared to data analysts, data scientists are much more technical, and possess an expertise in at least one programming language – R/ Python, data extraction, transformation, and loading capabilities. Skilled in data exploration, with a knowledge of machine learning algorithms, big data processing they are adept in data visualization as well.

• Machine Learning Engineer

Average Salary: US$115,000

Machine learning engineers create data funnels and help the tech team to deliver software solutions. They typically need programming skills, besides strong statistics in addition to the knowledge of software engineering. In addition to designing and building machine learning systems, machine learning engineers are also responsible for running tests and experiments that monitor the performance and functionality of such systems. Machine learning engineers know the concepts of computer science, software engineering, data analysis, feature engineering and the metrics involved in ML. They have expertise in maths and statistics and are knowledgeable in algorithm selection, and cross validation.

• Machine Learning Scientist

Average Salary: US$115,000

They research new data approaches and algorithms to be used in adaptive systems. ML specialists have the data science know how, including supervised, unsupervised, and deep learning techniques. They are hired under the titles of research scientist or research engineer. The knowledge skills include robotics and machine learning, cognitive science and engineering. They have expertise in mathematics concepts and mathematical models

• Data Architect

Average Salary: US$105,000

Data Architect's ensure that the data solutions are built for performance. They design analytics applications for multiple platforms. In addition to creating new database systems, data architects often find ways to improve the performance and functionality of existing systems, as well as working to provide access to database administrators and analysts. They are well-versed with applied math and statistics, data visualization and data migration and RDMSs (relational database management systems) or foundational database skills. Data Architects have the knowledge about database management system software, especially Microsoft SQL Server, NoSQL and cloud computing.

• Data Engineer

Average Salary: US$105,000

They perform batch processing or real-time processing on gathered and stored data. Data engineer are also responsible for building and maintaining data pipelines which create a robust and interconnected data ecosystem within an organization, making information accessible for data scientists.

Data Engineers know all about the ae tools and components of Data Architecture. Have an in-depth knowledge of SQL and other database solutions. They are experts into Hadoop-Based Analytics (HBase, Hive, MapReduce, etc.), coding, ML and various operating systems.

• Data Analyst

Average Salary: US$60,000

Data Analysts typically transform and manipulate large data sets.  They also aid in the decision-making process by preparing intelligent reports to communicate trends and insights. Data Analysts are proficient in a high level of mathematical ability, programming languages like SQL, Oracle and Python and ability to analyse the model and interpret data. Data Analysts are experts in problem-solving skills with a methodical and logical approach to plan work and meet deadlines.