Why I Quit my High Paying Big 4 Data Science Consulting Job

Everything was going great and streamlined.
I was the brain of the data science team at my workplace and that usually happens in a good company. When you have a talent, you get the recognition for it. And when I was working in PwC, I was a data science consultant there. And I was the mastermind behind all of the data science projects in which I was involved. My colleagues like Managers, Associate Directors, Directors, Partners, used to come to me and ask me various concepts and various application of data science and statistics. It was a very great phase of my career. 

Down the line, you have to think as to where this is going towards.
How this is going to be in medium to long term. When I thought of the medium to long term prospects, my career goals were slightly in different direction. I wanted a more senior kind of data science position, a more statistical data science position, a position which has more applications of economics and finance.

So, my projects involved a mix of data science, marketing, programming, business analysis, and much more. One specific project which gave me a lot of learning as far as 'R programming' is concerned, was a really game changing project for my career.
I learned a lot of programming just from that project. That project involved so many R codes, those codes were not simple, but like a puzzle. Once you unravel the key, everything starts fitting into place and then you understand the big picture and then you get the essence of the whole exercise and why it is done. So this was the scenario in my big 4 or PwC data science consulting job. 

The offer which I got after, is in a market research firm. The kind of work which I do in this firm now, is in the intersection of economics, statistics and data science. 

Everyone has their own personal journey. So, you might want to get into programming side of data science or you might want to get into the model development or statistics side of data science or you might get into a domain specific data science. None of these categories fit really well into my personality traits or my aptitude.
In my opinion, it is the mix of all these three and that is what really drives my career moves.
In future also, my career moves or project choices will be governed by that idea, that would refine my understanding of my expertise.

Microeconomics You Must Know

I have done B.Tech from IIT Kharagpur and Masters in Quantitative Economics from Indian Statistical Institute. I work in the intersection of data science, economics and statistics.

The discussion in microeconomics can be started with the conception of what is there to buy in the market, it could be books, movies, food, etc. Given the preferences and what is available in the market, one can only buy the preferred goods if he/she has the required money, i.e, budget.

Given these things, a lot of mathematical analysis goes through and then there are a lot of market imperfections.

There is a lot of agent-based study done, i.e, Game Theory when there is a strategic interaction between agents. It is a very interesting part of microeconomics.

Let's take a behavioural aspect of Microeconomics : 

Market for Lemons
There is a very interesting concept in microeconomics, called Market for Lemons. It is the economics of asymmetric information. Suppose that you go to a used car market or a shop and you have two kinds of Cars, one is a good and one is a bad. Obviously, in the beginning, you were unaware of the fact that the car is good or bad. So both of these cars will obviously have a hidden price now. The hidden price is hidden because you don’t know the type of the car. This computation can be done automatically that when one enters into a used car market, then he needs to average out the good and bad car. At the moment an individual averages out the good and bad prices, the price which you want to pay is sufficient for the bad car, but it is not sufficient for the good car. So, the good car sellers are not happy in the used car market, they do not have much of incentive to enter in a used car market unless there is a perfect price discovery. 

On studying microeconomics, it makes an individual gradually move towards psychology and behavioural economics. Now then the subject of economics becomes interesting. It no longer remains the usual political economy discussed in news which is generally considered as macroeconomics.

Economics Vs Data Science in 2023

The key concern in 2023 amongst students of Economics, is the argument that in which field, there are better job prospects, Economics or Data Science.

Looking at the scenario these days, both of these streams have become overlapping in nature.

Pursuing an advanced economics degree, Masters or a PhD, is a great route to get into high quality Data Science Jobs.

Full Stack Data Scientists

As per the current trends, the demand of full stack data scientists is very high. 

The work profiles of Data Engineer, Business Analysts and Statistical Experts, combined together form a Full Stack Data Scientist. And sometimes, a variation is Machine Learning Engineer. It could be a wise decision if you are hell bent on making data science career to think about what exactly is a full stack data scientist.

Significance of Economics

Even being in Economics stream, one can get into the field of Data Science. Studying economics gives the statistical tools, research aptitude and the subject matter of economics which is an ideal way to get into finance jobs.
On combining these skills with certifications such as CFA (Chartered Financial Analyst), FRM (Financial Risk Manager) or CAIA (Chartered Alternative Investment Analyst Association), you are going to be in great demand in the finance industry.

Altogether, it can be said that, economics exposes an individual to conventional & analytical along with creative jobs.

If one gets into a data science education, it invariably demands him/her to learn about the latest trends in technology. And economists possess impressive technical capabilities and exposure to latest technology in addition to some core skills required to be a data scientist.