Recently I spoke with Dinesh Nayak about his journey from being an IT professional to a Risk modelling quant. I asked him a couple of questions about his background and how he managed to get into risk modelling. Hope this is helpful to some of you that are interested to switch career to Credit risk modelling.
Until recently Dinesh worked as an IT Engineer for a service based IT company. After hearing from a friend about career in Risk Modelling, he started exploring career opportunities in this field. He contacted me some time last year (in Nov). I advised him to focus on the basics and complete my credit risk modelling course. He sincerely did the course and also clarified his doubts over a couple of calls with me. It gives me pleasure to say that he has managed to clear interviews at so many places (top notch companies such as KPMG, JPMC, EY).
Enjoy the below (rather short) conversation with Dinesh. For anything, reach out to me over email.
1- Tell us about your education and career journey
I am a Btech Graduate in Mechanical Engineering. I have done my BTech from a Tier – III college in India.
I have 5.5 years of experience in industry. Currently I am working as an Analyst in Wells Fargo. My previous employer is TCS.
2- What motivated you to get interested in Risk modelling?
It was a friend who told me about Risk Modelling. It was really interesting to know how the bank is mitigating different kind of risk.
That motivated me to get into Risk modelling as my work will have direct impact to the bank.
3- What challenges did you face in the interview preparation?
As I come from different background , initially I was facing challenges in statistics part. Statistics is a huge area and many captive banks were driving deep into statistics.
4- Can you share a few technical questions you were asked in the interview?
Below are the few questions asked in interview
1. What model would you use if you have continuous data and you want to forecast things?
2. How would you validate the model?
3. What is multicollinearity?
4. What is PSI?
5. How will you handle overfitting of model?
6. What tests would you run to find whether two samples are same or not?
7. What is Rsquare?
8. What all functions have you used while building a model?
9. How will you read AUCROC curve?
5- What questions were asked to you in the HR and Managerial round?
In HR and Managerial round they were asking basic questions like my aspiration, reason for job change, my strength and weaknesses 6- What suggestions would you like to give to those interested in getting into a career in risk modelling?
I will suggest them to learn any of the model and its framework.
Try and finish some of the projects which will give a simulation of what a project looks like in realtime.
Try to be clear of basics of Risk Modelling.
Learn any of the programming language (SAS or Python)
Quantitative Risk Modelling (QRM) professionals are in great demand in India. But there are not many people know about this field, even though a career in this is highly rewarding. Banks and Consulting companies – that hire quantitative analysts – are struggling to find talent in India.
It is not surprise that many in India haven’t heard of this field. Simply because, India has always been (and still is) known to be a hub of IT outsourcing and it is generally thought that other career options are less rewarding. But things have changed in the last decade, as other career options – especially in the service sector- have emerged. And QRM is one of them.
Here I talk about in brief about what Quantitative analysts do and how you can break into this field.
Quantitative analysts are the professionals that use Statistics and Mathematics to solve problems in Finance. They work in Banks, Non-Banking Financial Institutions, Hedge Funds, Insurance companies and consulting companies etc. The field is very similar to ‘Data Science’, but there are a few noteworthy differences. QRM is somewhat theoretical (one has to understand the theory well) and focused more on problems in Finance, while data science is broad (used in many fields including finance) and a very applied in general. It can be said loosely that QRM is a sub-field of data science, although the name ‘QRM’ predates ‘Data Science’.
QRM involves more of classical statistics and mathematical modelling and less of Machine Learning (ML). Although the use of ML is increasingly explored these days in QRM, classical techniques like Regression modelling, Monte Carlo simulation, Survival analysis, Stochastic Calculus, Non-linear optimization are more popular.
However, QRM is not the only field that uses these techniques. Almost all scientific fields (Science, Engineering and Quantitative Social science) use these in some way or the other. Hence anyone who has a degree in these fields can aspire to have a career in QRM. And, as a freshman, nobody will expect you have knowledge of Finance, although it helps in having an edge over other candidates (who are competing with you to get the same job). I recommend doing some coursework in Finance/Banking or reading about the basics on the internet (Wikipedia and blogs).
Many would argue it is not that straight-forward and you need to have a formal qualification in Quantitative Finance (QF). First of all, there are not many ‘high quality’ QF degree programs in India. The ones who go for a formal qualification in US/Europe do not return back to India (for obvious reason: they settle in those countries with better paying jobs). At least I have not come across many. Therefore, companies hiring in QRM in India have no option but to hire people with no formal qualification in QF. So who do they hire then?
They hire students and professional with strong background in Mathematics, Computer Science, Statistics and Econometrics. So you will find Economists, Physicists, Engineers, and Statisticians working as quantitative analysts in India. You can visit the LinkedIn profiles of many of the professionals (based out of India) and what you will find that most of them are from these fields. They have either a masters or a PhD in these fields and some have only a bachelor degree.
For long students from the quantitative field (science, tech etc.) didn’t know about the field. Only students from a few IITs were aware of it and hence they made most of the work force. You will also find students from Delhi School of Economics (DSE) and Indian Statistical Institute (ISI) working in this field. But these top institutes and students coming these places have many other career options to choose from. A computer science (CS) graduate from IIT may want go for a career in Software development. A statistician from ISI may want to do PhD to become a professor. By the way, ISI and DSE do not produce graduates in bulk. Only a few hundred students come of out of these institutions. Therefore, there is a huge talent supply gap in this field.
Owing to lack of supply of good talent, and that the job is high skill nature (you need to be good in Maths and Programming), the salary is quite good, especially for people with prior work experience in this field. To give you a ball park figure, you can expect to earn around 30lakhs with four to five years of work experience. With 10 years of experience, you could expect to earn more than 40L. The good ones make well over 50L. A quantitative analyst, on average, makes more money than a data scientist.
The biggest challenge is to get some experience in this field. Freshmen, unless from top places like IITs and ISIs, will find it difficult to break into this field. It is not impossible, however. Like in any other field, you have to work hard to get your first break. Things are much easier thereafter.
The advice I would give is no different from anybody else will give for any other career. You have to learn the skills required for the job (Statistics and coding). Communication skills (written and speaking) are very important as well. Above all what is most important reach out to someone who is already in this field to get some guidance? I am not an expert, but since I have been in this field for a long time now, I can give an advice or two
– Learn these maths topics: Linear algebra, Calculus, Hypothesis testing, Regression analysis, Time Series analysis
– Learn more than one computational programming languages (R, Python, and C++). It goes without saying learn SQL.
– Attend Quantitative Finance workshops. RBI and IGIDR together conduct an annual QF workshop. Check their websites.
– Connect with more and more people on LinkedIn who are working on this field
– Do internship in this field. Just reach out to people (write cold emails) for internships. Finding an opportunity in Big4 consulting firms (EY, KPMG, PwC, and Deloitte) is easier than bank. Try interning at the Fintechs.
– Do QF courses on Udemy. If you can spend a bit more, you can do course on Coursera/EDX. These courses may not add much value to your CV. But the things you learn from these course will be of great help in the interview.
– Do certifications (FRM /PRM/CFA). Not everyone can do these courses (due to lack of time and money). But if you have the resources at your disposal then do either of these courses. You can also do CQF (but that’s super expensive).
– Make use of the free resources on YouTube/Blogs. Unlike Data Science, there is not a lot of free content on QF. But you still can find a number of good videos (ex. in MIT OCW). Many ex-quants write blogs. Check them out.
– Join QF communities on social media platforms (Facebook, Quora, and Linkedin). You may follow famous quants on twitter to know the latest on this field.
– Start a blog/YouTube channel and start writing/making videos on your learning experience.
For more information, you can contact us directly. Write to us if you want guidance on career in QF (mentorship, CV preparation, interview preparation etc.). You can reach us at firstname.lastname@example.org.