What made you choose the data analytics sector amid many rising non-engineering fields?
Ans: People in the current world are always online. They are browsing, shopping for goods and services and transacting. Data collected is being increasingly used by businesses for their growth and survival. The ability to analyze high-velocity data at scale has become a coveted skill in large organizations. I chose this career to develop this skill. Big data analytics combined with business knowledge gives an incomparable edge in one’s career.
How did you stay motivated and stick to data analytics?
Ans: Given that the field is ever evolving, every day is a new challenge. There is much to be explored and it keeps you on your toes. In the year that I have been working, I have never felt bored with my work and challenges keep me motivated.
Could you mention in a good detail, about the kind of work you do?
Ans: I work for Citi Corp, with the Fraud Prevention team. We collate customer data, personal as well as transactional data and analyze the patterns to detect anomaly, and point to fraudsters. This is my first job. The team within an established company works on very innovative projects. There is some research involved when a new project is being started, at times it feels like working for a research project under a professor. Transition from college to the job was smooth also because the team is comprised mostly of freshers from reputed engineering colleges.
The team deals in US credit card business, so the work timings are from 1 PM to 10 PM. Depending on the difficulty level of the project and your interest in it, hours can stretch a bit. Average work week would be of 50 hours. Working on weekends is typically not required. There is an average level of work-life balance.
Big Data tools like Map Reduce, PySpark, Hive, etc are employed for analysis of customer data. Spending pattern analysis, Merchant analysis, analysis of digital signatures etc. are done on a regular basis in the team. Models to catch customers with fraudulent intent are developed. Network science is used to create connections between accounts. Newer projects are also using Machine Learning to develop these models.
Does the work get monotonous after 2-3 years?
Ans: The job profiles don’t change by much initially. After a good 5-7 years in the field, one will assume a more supervisory role. Soft Skills will be developed only in the later stage.
How does promotion work? How fast or slow is the process? How do the job profiles change post promotion?
Ans: In the initial stages, promotions are more or less given after fixed periods of time, typically after 2 years of working. If an employee performs exceptionally, they can be promoted a bit sooner. The job profiles don’t change by much initially. After a good 5-7 years in the field, one will assume a more supervisory role.
What are some exit options (MS, startup, something else)? What comes after availing of these options?
Ans: There are plenty of exit options. MS in data science, computer science is a very viable option. Start-ups also need to analyze Big Data to determine consumer trends. Some startups as well as big corporations require professionals with working knowledge of Machine Learning. These are highly skilled jobs, with a very handsome pay. People also opt for an MBA to develop their managerial skills after a few years.
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