Here is a peek at trending roles in Data Science across industries
Hailed as the sexiest career of the 21st century, Data Science has emerged as one of the most sought-after and competitive fields of today. As the accuracy and efficiency of data-driven decision-making increases, the demand for data science experts has seen exponential growth.
While new jobs and roles are cropping up every day, existing jobs are also evolving through the use of more data analysis, requiring professionals to add more advanced skills to their portfolios.
Therefore, for a recent graduate or a mid-career professional seeking to build a career in Data Science, it is imperative to have a thorough understanding of the job market to know what skills they need in order to successfully do so. Let’s take a look at some of the most trending roles in Data Science across industries.
There are a plethora of profiles that analysts can find themselves working in, including Data Analyst, Business Analyst, Marketing Analyst, Systems Analyst, Operations Analyst, Quantitative Analyst, and Business Intelligence Analyst.
These are entry-level roles at the lower end of a company’s hierarchy, essentially involving churning out insights from various raw data sets to make strategic recommendations for the business.
A data analyst typically deals with more data, and therefore more sophisticated data analysis techniques, than a business, operations or marketing analyst. A business analyst, though, may climb up the ladder to become a specialised Business Intelligence (BI) analyst.
Key skills required: R, Python, or C/C++ with SQL-based database management specialisations.
A qualified data engineer designs the system responsible for making raw data application-ready for data scientists and analysts. A data engineer engages with the database available and performs data processing.
The focus is more on data crunching and interactions with the database to establish the system infrastructure. This infrastructure is, in turn, used by an analyst to derive meaning out of the pile of data.
Key skills required: A background in software engineering with fluency in programming languages such as SQL, Java, R, Matlab, Python, SAS, SPSS, Ruby.
As an architect in the field of data science, there are several roles to explore – data, data warehouse, applications, infrastructure, and enterprise. An architect ensures healthy interactions, integrity and security of the domain he is responsible for.
For instance, a data architect facilitates database protection, maintenance, and efficient information retrieval.
Similarly, an applications architect ensures healthy interactions between various applications running in a software system along with monitoring their real-time behaviour.
Infrastructure and enterprise architect roles, being more managerial and less technical, ensure optimal functioning of the company’s infrastructure and safe-keeping strategies and resources.
Key skills required: SQL, Hive, Spark.
Data Scientist is the most specialised and sought-after job in this area of work. As a data scientist, one must be able to decipher an ocean of data, find hidden trends and patterns in it and then communicate it in an easy-to-understand manner.
This specialised role needs an amalgamation of machine learning, data mining, analytical and statistical skills.
Key skills required: Programming ability and fluency in a sophisticated platform (such as Matlab, R, JuPyter) must be second nature. Business or domain knowledge is a distinct advantage.
Machine Learning Engineer/Scientist
A machine learning engineer is responsible for creating models, researching new data-science based approaches and employing statistical algorithms and data to generate key insights that help in delivering business solutions.
Key skills required: A background in programming, statistics, data modelling, machine learning algorithms, software engineering.
If you are a fresh graduate or still at a nascent stage in your career, an internship is often a fantastic opportunity to work with an organisation’s data scientists, machine learning engineers and analysts, and learn from their rich experience while working on a hands-on project.
Additionally, one can also explore freelancing and entrepreneurial opportunities, after having gained solid skills in the domain.
Almost all companies require a data scientist in today’s digital world but don’t necessarily have the resources to hire a full-time specialist. This opens up spaces for freelancers that can lead to establishing an independent consultancy.