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)
Since Data Science is new, salary negotiation is a tricky affair in this field. Not much is available on the salary offered in data science on public domain. So benchmarking is not always possible while negotiating the salary.
There are some general negotiation techniques that are applicable to all jobs. However, you must know some specifics to data science. This article is about what specific things to consider while negotiating salary in data science.
Here are the tips you should keep in mind while negotiating your salary
Experienced people have higher bargaining power: Since data science is a new, not many have long experience in this field. Hence there is a shortage of experienced professionals. If you have more 5 years of experience in data science, you have good bargaining power.
Not all Sectors pay equally: All sectors employ data scientists. But some employ more than the others. For example, banking sector employs a large number of data scientists, as it produces massive amount of data and data based decision making is mandated by banking regulation. Moreover, banks normally hire those that already have banking experience. So you have higher bargaining power in banking/financial sector. Technology and Ecommerce companies pay high salaries, compared to say tradditional manufacturing companies.
Some skills are hard to find in the job market: If you are a data scientist with strong experience in computer vision, NLP, deep learning (so called high end Machine Learning fields) then you can command higher salary. If you have experience in some proprietary software (such as SAP/SAS/Matlab) then also you can command higher salary.
Secondary Skills play a big role: If you are a data scientist with prior software development experience, you can ask for higher salary. It is difficult to find data scientists that can develop software from scratch. If you are data scientist with marketing or sales experience, you will be highly valued in many places (such as retail/ecommerce). So if you have good secondary skills, ask for more money.
Higher Education(M.Sc./PhD) level matters (though not in all fields): In many sectors, data scientists need to have a higher education at least at the level of a master degree. For example, in banking sector one has to be have a master/PhD degree to work in the regulatory modelling area. This added requirement makes it harder for banks to hire data scientists. Hence they pay higher salary. Consulting companies value higher education (and certifications) a lot, so that they can impress thier clients. So if you have PhD or any other graduate degree from top universities, you will be in great demand among consulting companies.
Some General Tips:
Do your research. Learn the average salary for your experience level from glassdoor and other sources online. You may talk to your friends and colleagues that is not an issue.
Talk to some HR folks in this field. They normally have good overview of the current market trend in the job market
Do not quote your expectation first. Let your potential employer quote it first.
Your currently salary does not matter for your future salaries (especially if your current salary is not very high)
I get asked this question all the times: “I have several years of software development experience. How do I become a data scientist”? Many IT professionals are interested to switch career to data science but do not know how to do so. In this article, I will cover this topic.
To begin with, let me share my own story. Back in 2008, I was an IT developer. I was working for a leading Indian IT company, as a PL/SQL developer, for the Enterprise Resource Planning (ERP) implementation projects for a leading global client (GE). I was working in Inventory and Order Management (modules of ERP). That is where I saw algorithms/models being used for decision making (ex. Inventory forecasting), and I got interested to know more.
However, I did not know where to start. I could not find any online course on this. In fact, online learning was not very popular back then as they are now. Note that these are pre-data-science times. With no other option at my disposal, I decided to quit my job (in 2010) to do a full time M.Sc. in Economics (to learn Statistics/Econometrics as part of this course).
Back then I did not know ‘data science’ as a career (now we all know). In fact, this term had not yet been coined. But there were jobs in data analytics (rather a niece area back then) in few companies. To get these jobs, you needed to have a (post graduate/PhD) degree in Statistics, Mathematics Economics. It wasn’t even easy for engineering graduates from elite universities to get these jobs (let alone others).
Long story short, I managed to get a data analytics job at a US based MNC after finishing M.Sc. in Economics. It has been almost a decade since I am in this field.
Things have changed so much in a decade time. Now anyone with a quantitative degree can become a data scientist, provided he/she is willing to learn things freely available on the internet. The entry barrier is less now compared to when I started my career in Data Analytics. Besides, the demand for data science professionals has increased leap and bound.
So what steps would I take if I were to start all over again?
I would not go for a full time course (for sure). I already knew coding and was from Physics background (knew enough mathematics). So there was no point wasting two years and good amount of money (being from a lower middle class family) doing post graduation. I would have self-taught myself to become a data scientist. I do not think I would be any less competent data science professional than what I am now had I taken this route.
That is what I suggest anyone who is already having few years of IT experience. Do not leave your job to get into data science. You can learn on the side while working full time. Unless you are rich or interested in a career break, doing a (full time) master degree in data science is not worth it.
So how do you learn on your own to become a data scientist? Follow the below steps
General advice:
– Do not think you will become a data scientist overnight. It takes time. Usually it takes about 4-12 months of rigorous study to get a job in data science.
– Plan your study. Gather resources from where you will study. Do not follow too many resources. A few good ones are enough.
– Do not waste time understanding confusing (technical) jargons (like difference between data science and data mining).
– Do not fall prey to training centres that promise you placements (most do not provide).
– Do not be intimidated by technical terms. You will be comfortable with them over time.
– Do not do data science because everyone else is doing. Other IT jobs are equally rewarding.
– Do spend some time researching if you are genuinely interested. Else do not waste your time. Talk to your friends/colleagues who are already working in this field.
– Do not expect huge salary increase in a short time. You will be disappointed.
– Data science is very vast. So you do not have to know everything. So focus on just a few areas and try to be good at them (you need not be good at both NLP and Computer vision).
– Do not worry if you are from Non-engineering background. I have seen many from pure science, arts and commerce streams doing well in data science.
– Remember your programming experience (Java, .Net, C++ etc.) or Cloud computing experience will be invaluable in data science.
Specific advice on how to learn:
– Learn Linear algebra and Co-ordinate geometry on Khan Academy (1 week)
– Learn basics of Statistics (Mean, Median, Correlation etc.) on Khan Academy (1 week)
– Learn basics of Python Programming on freeCodeCamp (2-3 weeks)
– Learn basics of SQL on w3school (1 week)
– Learn advanced Statistics theory on Statsquest (Linear Regression, Logistic Regression, Quantile Regression, Polynomial regression, Hypothesis testing, Time Series analysis, Cluster Analysis) (4-6 weeks)
– Learn Machine Learning on Statsquest/Krish Naik (Decision tree, Random Forest, Bagging, Boosting, Support Vector Machine) (4-6 weeks)
– Learn implementing these models in Python on Sentdex (8 weeks). Also do a few basic projects (predicting survival using Titanic dataset, stock price forecasting etc.).
– Learn advanced ML libraries in Python on Sentdex/Krish Naik (2 weeks)
– Do more advanced (real world) projects (twitter sentiment analysis, Credit scoring in banking etc.). (4 weeks) — contact us for projects.
– You may participate in competitions on Kaggle (but you do not need to do well there to become a data scientist)
– Learn some data visualisations on Edureka (Using R/Python or specialised tools like PowerBI). Big data/ETL experience also adds values to your CV.
The above mentioned things can be learnt in 5-6 months. After you have done the above, start applying for jobs (internally within your company or outside). You can never gain good experience doing projects at home. So try getting a project within your company or outside. Highlight your (academic) data science project experience in your CV. More than CV, people will be interested in your projects. For example, if you have done a Sales forecasting project then it will interest retail companies. A credit scoring project on your CV will impress banks/fintechs.
These are some of the wonderful free resources (blogs/YouTube channels) to learn data science
– Sentdex
– FreeCodeCamp
– Krish Naik
– Edureka
– Machinelearningmastery.com
– Khan Academy (for maths/stats topics only)
My personal favourite is FreeCodeCamp. If you do not want to follow too many blogs/YouTube channels, just follow FreeCodeCamp. They have everything you need to become a data scientist.
I also run a YouTube channel (Analytics University). It is not as good as the above sources. However, if you are an absolute beginner and you have no issue with Indian accent, you may follow my channel. There are many beginner friendly analytics videos (on Python, R and SAS).
While free sources are wonderful (no doubt), they are not very organized. Even the good channels/blogs are not very organized. Hence I suggest you to do a few cheap online paid courses. These are many courses on Coursera/Udemy. You may subscribe to DataCamp as well. Paying a little money, you will get to learn from amazing faculties (in an organized fashion).
If you are planning on attending bootcamps, prefer offline ones. Crowded online bootcamps are not that great (telling from my experience).
I recommend people to hire a career coach. It’s not that expensive. There are many who are providing career coaching (data science related). Talk to them over phone/zoom on a regular basis to guide you and clarify your doubts (technical as well as non-technical). Also ask them to review your CV and provide you with interview preparation tips. You may hire me as a coach (contact: analyticsuniversity@gmail.com).
Sometimes, you may not have to learn all that I have mentioned above to start applying. You just need to do a couple of projects in a given field and then you will start getting calls. Five years back I trained someone, who was an absolute beginner, in Supply Chain Analytics for 2 weeks. He managed to get a job with PwC in a month time. Another person, who was working as a tester with IBM, managed to get a Financial Crime analytics job after doing a Credit scoring course form me (contact me if you are interested to learn Credit risk modelling).
My first student (back in 2014) was an experience IT professional (10 years more experienced than me) who managed to become a data science manager at Cognizant after learning some basic stuffs from me. He is not a hands-on data scientist, but he has enough knowledge to manage data science teams (ideal for experience > 10 years).
The bottom-line is that you need not become an expert to break into data science. So do not feel intimidated by reading on different blogs that you need to master 100 different techniques before calling yourself a data scientist. Like software engineering projects, there are many types of data science projects. Some are extremely complex, others are moderate to easy ones.
But there is something for everyone in this field. So if you are genuinely interested, I suggest you give it a shot at it (and now). This field (despite over-hype) will only going to grow in the future (imho).
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 analyticsuniversity@gmail.com.
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