Data is the new oil, and many product and service companies have become data-first companies because data helps companies identify the right target audiences, forecast demand, and optimize deliveries. Due to the value data brings for a company, there is a massive demand for people with expertise in data science and analytics. As a result, hiring for this domain is expected to splurge, and it is expected to become one of the most lucrative job opportunities with a huge paycheck.
Few firms that are going to hire a lot of Data Science and Analytics experts are as follows
Hyperlocal Delivery startups
To maintain a competitive advantage, hyperlocal delivery startups like Zepto, Dunzo, Grofers, etc., will hire many data experts. These startups have to deliver groceries in the minimum possible time, and hence they need to do a lot of data crunching to forecast demand to update inventory. Data also helps to optimize delivery time by doing path optimizing and supply chain optimization by prioritizing the products in demand. The average salary for a data analyst in these firms is around 12-15 LPA, and for a data scientist is 16-20 LPA.
Neo banks are a layer over the conventional banking system that helps users track expenses and income. Neo banks like Fi, Jupiter, Uni, Slice, etc., will hire many data scientists to analyze income, spending, and savings and sell financial products like Mutual funds, which can give them returns in the future. The average salary bracket for data analysts is 10-14 LPA, and for data scientists is 14-20 LPA at entry-level.
Companies like Zerodha, Wint Wealth, Golden Pi, Groww, Smallcase Coin DCX, etc., are seeing record signup and investing numbers, due to which they are expected to hire a lot of data-savvy people. The need in such fintech firms is two-fold because data insights allow them to introduce new features for the users and increase the user base and suggest users the best fintech products to earn more and more revenue. The entry-level salary for data roles in such companies lies in 10-20 LPA.
Difference Between Data Analysts and Data Scientists
You must be wondering how data scientists are different from data analysts because they are getting higher average salaries. Data scientists do a lot of forecasting and modeling using Machine Learning models, whereas data analysts draw insights from the pre-existing data that the company produces. Data analysts use languages like SQL, R, and Python. Even if you start as a data analyst and keep upskilling, you can become a data scientist.
In data-heavy roles, you will be crunching many numbers and interacting with massive databases daily, so it is crucial to have good technical skills. But soft skills are also crucial because until and unless you can explain the insights, they won’t be helpful to the company.
To prepare for data roles, you can practice R, SQL, Python, Machine learning on websites like Geekforgeeks, Hackerrank and then go for internships in startups. Knowing probability and statistics is a plus because it helps forecast and model. You can take courses for the same from platforms like Coursera. The internships would help you with your resume and help you in the interview because the experience of handling a real dataset is way above solving problems on an external website.