Data science is getting bigger, courtesy massive data inflows and the need to extract great insights to guide company operations. Professionals have a number of interesting roles to pick from.

Data is everywhere! Businesses across the world are constantly generating data on various parameters of their work, and this data is often used to extract valuable insights that could guide the future course of action. Data science helps to do this, allowing businesses to understand consumer behavior and accordingly tweak their operations, products, and services, and more. It is no wonder that Max Levchin, co-founder of PayPal, said: “The world is now awash in data and we can see consumers in a lot clearer ways.”

A lot of companies are on the lookout for data science professionals!

A January 2019 report from job site Indeed showed that the demand for data scientists has grown at a CAGR of 29% from 2013, with an absolute growth of 344%. Similarly, data from technology job site Dice showed the number of data science job postings on its platform – as a proportion of total posted jobs – has increased at a CAGR of about 32%. And, LinkedIn data in 2019 showed that data scientists have a median base salary of $130,000, and saw 56% more job openings this year than in 2018. Data science certainly looks like a great career choice!

However, the Indeed data also revealed that searches by job seekers skilled in data science grew at a slower pace (14%). This suggests that there is a fair gap between the supply of and the demand for data scientists. And therein lies the opportunity! Data science has caught the attention of jobseekers, with fresh graduates striving to get into data science and seasoned professionals putting in efforts to learn and transition to a data science career.

Why, though, is there so much demand?

Because there is so much data! The demand for data science spiked almost instantaneously, as companies got their hands on large volumes of data. Although data-driven decision-making has been a key part of operational strategy at successful organizations, it has now become mainstream. Companies can now use this data to understand customers and their behavior, sort out operational challenges, and build better employee retention strategies.

Problems previously solved with guesswork or hit-and-trial methods are now tackled by data analysis – collectively called data science. This combines statistical techniques, programming, and sophisticated machine learning algorithms to dissect problems to their simplest form, and derive solutions. Thus, companies need to hire people skilled in data science.

I bet job-seekers are queuing up!

Certainly. To take advantage of the demand, professionals are moving towards data science certifications, training, workshops, and seminars. They want to put in their best to equip themselves with the required skills.

Which roles can a data science professional take up?

Contrary to popular belief, ‘data scientist’ is not the only role available in the data science field. There are many other roles, with differences in roles and responsibilities, as well as in technological and other skill requirements. A common career path is a progression from ‘analyst’ to ‘principal data scientist’, and a typical data science team comprises analysts, data scientists, senior data scientists, and principal data scientists, among others.

Some of the common roles in data science are explained below:

  • Data analyst: looks after manipulating, processing, and visualizing huge amounts of data. This role also requires querying the databases as and when needed, as well as optimizing algorithms i.e. creating and modifying algorithms to cull the requisite information from databases while maintaining the latter’s integrity. The role requires competence in technologies such as Python, R, SAS, and SQL, along with strong problem-solving skills.
  • Data engineer: design and test big data infrastructure for the business such that data is kept secure and analysts and scientists get access as needed. The systems must be optimized and stable so that algorithms can run smoothly, and could be upgraded with newer or upgraded technologies. The role requires competence in technologies such as C++, Hive, Java, Matlab, NoSQL, R, and Ruby, along with the ability to work on data APIs and Extraction, Transmission, and Loading (ETL) tools.
  • Machine learning engineer: takes care of building data pipelines; classification, clustering, and other machine learning (ML) algorithms; and A/B testing. The role requires expertise in REST APIs, SQL, or similar technologies, along with sound knowledge of Java, JS, and/or Python, and strengths in mathematics and statistics.
  • Data scientist: Extract relevant data, clean it, and analyze it to derive insights. The role requires strong analytical skills, knowledge of statistics, and machine-learning algorithms, along with proficiency in programming languages such as R, Python, MATLAB or SAS.
  • Business analyst: identifies how big data can be linked to actionable business insights for bringing about business growth. This role involves separating high- and low-value data, along with understanding the working of data technologies and handling large data volumes. The role requires competence with data modeling, data visualization, and/or other technologies, as well as a strong understanding of business finances and business intelligence.
  • Data and analytics manager: overseeing data science operations. The role requires competence with data science technologies such as R, SAS, and SQL, along with management skills.
  • Data architect: requires understanding of data warehousing and database design. This role looks to help companies design secure, reliable, and manageable data management solutions. The role requires experience and expertise in data modeling, data warehousing, ETL, and other big data tools, along with a strong grasp of Hive, Pig, and Spark.
  • Database administrator: ensures proper functioning, backups, and recoveries of databases, along with managing employee access as per need. The role requires competence in skills such as database backup and recovery, data modelling, and data security.
  • Statistician: extracts insights from data clusters and helps to create new methodologies for data engineers. The role requires experience with data mining, database systems (such as SQL), and ML technologies.

The future seems bright!

It is. After all, Tim Berners-Lee, known as the inventor of the World Wide Web, said: “Data is a precious thing and will last longer than the systems themselves.”