Humankind has been through several iconic eras over its course of existence – from the Stone Age and the Industrial Age, to today’s Information Age. And just like each of these eras were dominated and characterised by something specific, so is today’s modern world. Data or information, is what the entire world runs on today, utilising the seemingly endless power of technology and the internet. As such, Data Science, which involves collecting, manipulating, storing, managing, and analysing data, to help organisations make business decisions, is one of the most promising fields of today. The rising demand for professionals in the field of Data Science, with more and more technological advancements taking place in the world, makes it the best time to pursue careers in the domain, which will certainly play a huge role in shaping our future. Listing down some of the career tracks in data science, in India most of these roles are merged in start-ups and mid-size organisations.
1. Data Scientist
Data Scientists are responsible for analysing small and big data, identifying, cleaning, and organising big data for companies. Their job involves and building data products like recommender systems which are used on platforms like Amazon or Flipkart. Data Scientists use advanced analytic technologies like machine learning and predictive modelling, they comb through vast quantities of structured, unstructured, and semi-structured data, to recognise definitive patterns. Based on the findings of their analysis, they provide pertinent insights beyond statistical analysis to help an organisation make strategic business decisions and gain a competitive edge.
2. Business Analyst
Business analysts are the old graduates of this field, they analyse data primarily on the basis of the business decision makers. They have a great knowledge of the organisation and the business domain of various industries like telecom, retail, health, ecommerce, manufacturing along with the various functional areas like marketing, finance, HR, operations etc. Business Analysts provide insights based on data that helps answer questions like, “why some product failed?”, “what will be the sales for the coming quarter?” etc.
3. Business Intelligence (BI) Developer
Considered one of the most coveted Data Science professionals in the corporate world, Business Intelligence (BI) developers are responsible for designing and creating strategies which help in making better business decisions. They either use existing BI analytic tools, or develop their own, to make the understanding of system operations easier. They are also in charge of regularly developing and enhancing IT solutions, by coding, designing, testing, debugging, and implementing such tools.
4. Data Engineer
Data Engineers share a symbiotic relationship with Data Scientists, as they are responsible for making the data readable for them. Not only do they create and maintain the analytics infrastructure, powering almost every function in the data domain, they also create the data set processes used in modelling, mining, acquisition, and verification. They handle the development, construction, maintenance, and testing of architectures, like databases and large-scale processing systems, and perform real-time processing of the accumulated data. Using available or self-created data analytics systems, they work towards enhancing the quality and quantity of data as well.
5. Machine Learning Engineer
Along with creating data funnels and delivering software solutions, a Machine Learning (ML) Engineer is also responsible for researching and implementing appropriate ML algorithms and tools. They also design machine learning systems from the ground up, by studying and transforming data science prototypes, and selecting appropriate datasets and data representation methods. Through repeated machine learning tests and experiments, they perform a statistical analysis of the systems and fine-tune their operations, as per the test results.
6. Data Analyst
A Data Analyst is responsible for tracking web analytics, analysing A/B testing, manipulating and transforming large data sets, to be in line with the expected analysis for companies. By using statistical methods to analyse data, they generate insightful business reports, and recommend new ways to reduce expenditure by improving the efficiency of business processes. They also work closely with management to create a priority-based list of business and data needs for each of their projects. Using the available data, they also create models that showcase customer trends and the consumer population, as a whole.
7. Enterprise Architect
By working closely with stakeholders, including management and subject matter experts (SME), an Enterprise Architect handles the creation, maintenance, enhancement, and management of IT architecture models and IT support systems, along with their lower level components. They also evaluate a company’s business strategy carefully to create an IT systems architecture that supports it.
8. Data Architect
A Data Architect is responsible for the development of data solutions for multi-platform performance and design analytics applications. By carefully analysing database implementation methods, they ensure compliance with company policies and external regulations, maintaining the integrity and security of the company database. They also provide insights into the changing requirements for database storage and utilisation, along with offering possible solutions and suggestions to streamline the same.
Studies conducted by McKinsey Global Institute show that big data can not only help increase the profit margin of a retailer by as much as 60 percent, but also save consumer expenditure. This has a healthy, far-reaching impact on the economy, especially since data science experts and professionals are in demand in almost every vertical today. Therefore, there remains no doubt that Data Science is indeed slated to be the fulcrum for economic transformation, in the near future.
Author: Bhupesh Daheria