Podcast Link : Quantitative Finance Professional Podcast

Financial systems in the world are designed to ensure that money flows continuously in order to meet the needs of people, society and economic growth!

Rahul Jindal, our next pathbreaker, Associate Quant at PGIM Multi-Asset Solutions (New York), builds financial models that help portfolio managers make investment decisions.

Rahul talks to Shyam Krishnamurthy from The Interview Portal about exploring several careers such as mechatronics and data science before discovering his interests in quantitative finance !

For students, don’t be afraid to challenge yourself, take risks early in life. When you are young, you have less to lose.

Rahul, can you talk about your growing up years?

Hi, I am Rahul Jindal. I am currently working as a Quant at PGIM (a global asset manager with over $1T assets under management) in New York. We help our clients manage their investments in various assets like Public and Private bonds/stocks as well as real estate.

I grew up in Bathinda, Punjab in a family full of engineers. I was always interested in computer gaming and spent a lot of my time on computers. I was moderately active outdoors and played soccer for my school. I wanted to be a software engineer/game developer when I was in school. I have an elder brother and cousins, so exposure to computers came at a very young age. When it was time for college, robotics was getting very popular, and I started to dream about coding robots and automating machines. 

What did you do for graduation/post graduation?

I did Mechatronics/Robotics for undergrad. Then I did a master’s in Data Science from Carnegie Mellon University. Lastly, I did another masters in Quantitative and Computational Finance from Georgia Institute of Technology.

What were some of the key influences that led you to such an offbeat, unconventional and rare career in Quantitative Finance?

I was lucky enough to get enrolled in BTech in Thapar University’s Mechatronics program. I spent 2 years developing deep expertise in robotics and learning the basic skills (coding/3D designing/testing etc). I soon realized that robotics in India was less focused on algorithms and more on the manufacturing/mechanical side. I decided to pivot out by joining extracurricular activities at Thapar. I was involved in event management, finance etc to a point where I led a few such organizations. This helped me a lot in exploring my business side and improved my interpersonal skills.

Soon, I was introduced to Data Science/ Machine Learning. I fell in love with the field as it was very similar to robotics algorithms and enabled me to use my business acumen. For the next 4 years, I was deeply involved in mastering the field. I did a master’s program and worked at a couple of places to gain more industry experience. During my masters, I was exposed to finance as a subject. That’s when I realized I could connect my business interest in stock markets and my skill sets and become a quant professional. I was always good at math. I was already coding for 8+ years and had interests in the markets (everything required to start a quant journey). I decided to pursue another masters in quantitative finance, where I learnt a lot more about the markets, the financial mathematics and theories, and got to apply those during my internship. Finally, after graduating, I joined PGIM and have been here since. I love what I do – math/complex puzzles/probability/finance and coding.

You might notice, I have changed my career multiple times and have had a variety of experiences. I actually wanted to do that. I love to explore (my father would say I get bored of things very soon). I have always learnt by exploring and experimenting. Even when I was making different moves, as you would notice, I was always connecting the dots. I was identifying my weakness/areas to improve and trying to build on that. I have always believed in widening and deepening my skill set and luckily that has always worked for me. I can’t point to any events/people or even turning points. All I will say is I explored what I thought I would like, tried it, if I liked it, I went deeper, else I moved on. I thought less and did more. I believe that’s what worked for me.

How did you plan your steps to transition to a new career?

I believe the network plays a big role here. I was very lucky to have always been surrounded by very smart people, at home as well as outside. That taught me how to survive in competition. And to be honest, competition is everywhere, and we should not take competition as something negative. I could have not done what I did without competition. I always love to be in a room where I am the least smart person because there is so much to learn. After I leant to be uncomfortable, things were not that difficult (even though they were 😊). 

My approach was to get challenged at every step. The first challenge was robotics, then event management, then data science and now quant finance. Though I dealt with few of the most complex problems in the world, I enjoyed my journey. The secret recipe was to keep trying new things, keep learning and keep connecting the dots. Once you know what you want to do, figuring out how to do it is a much smaller problem. That’s how I kept building my skillset. From academics to professional experience, I had the same approach. That’s how I got into schools I wanted, courses I wanted and then companies and roles I wanted.

After my 1st masters in Data Science from CMU, I was very confused between getting a PhD (no tuition fee; stipend of about 30-35k USD; higher degree; but, 5-6 years commitment to Academia) and doing a second masters (high tuition fee, not adding a higher degree to my resume, and a 1.5 years timeline).

Since I always wanted to get back to the industry and was not inclined towards working in the Academia (which a lot of the PhDs end up doing), I believe the short timeline of a master’s program was very well suited for me. I always preferred a masters and 4 years of industry experience over having a 3 letter degree.

Once I decided to pursue that, I started to look for options. Now, Quant Finance is not a pure finance field. It’s an interdisciplinary field with Math, Stats/Probability, Computer Science and Finance. I wanted a school that is good at all or most. GT (Georgia Tech) is one of the top names in the US for Computer Science and Operations Research/Engineering. GT has a quant master’s program which according to my rankings would definitely rank within the top 10 quant programs in the US. At last, I had to choose between GT and UCLA; and I ended up at Georgia Tech because of a more tech/math heavy curriculum.

How did you start your career in Quantitative Finance?

After my BTech, I joined Mu Sigma, I was trying to get into a data science path in the financial industry, thinking that is what quant finance is. That’s when I decided to do my 1st masters in BI & Data Analytics from CMU. When I was at Carnegie Mellon, I took many financial management electives and got exposed to some finance fundamentals. Later, I graduated during peak covid, and no one was offering jobs to fresh grads. Through some network connections, I met the directors of a stock brokerage firm Taylor Collison in Adelaide. It was a fundamental stock brokerage with a small equity research department. No one coded at TC. They were looking for new revenue initiatives and wanted to try quantitative signal research with a few ideas they had in mind. That’s how I got my first quant job. I worked there for free for a month and then later got a 3-month contract offer from them. In those 3 months, I built our first signal research engine and started talking to potential clients. In quantitative finance, a signal is an indication to buy, sell, or hold an asset. It is based on quantitative models that analyze various market data, including price, volume, volatility, or macroeconomic variables.

I was right out of college, no savings and at a very risky place as we were not making any money with what I was doing. I was looking for a more secure job and that’s when I got into the Government of South Australia, Superannuation/Pension Fund department. Unfortunately, that was again a very small stint as I decided to pursue my second masters and move back to the US.

Tell us about your first break?

I would say my first job at Mu Sigma after my BTech. I was working as a data scientist. I was responsible for building complex machine learning models. At a very young age, I was asked to handle the end to end process of problem solving. Starting from talking to clients (who had more experience than my age), gathering requirements, solving problems by building models and then presenting results to the client and seeking feedback. It was a great confidence booster once I was able to do all of that. This helped me grow my skillset as well as taught me how large businesses work. It was a great blend of technology, business, and math. I definitely still use the learnings from that job in my current role and I believe I will continue to do so throughout my career.

I landed the job at PGIM 4-5 months before I graduated from GT. The application process was through LinkedIn, followed by 4 very technical interviews.

PGIM Portfolio Advisory was starting a new quant team and was looking for someone with modeling experience along with the ability to build the ground level data/assumptions infrastructure. For me, I have always loved to work for a small team and help them grow from scratch. I believe it was a perfect match, in terms of roles and responsibilities. I liked the difficulty of the interview process, ensuring me the job quality and that I would be working with very smart people. I was very impressed with my boss and his experiences.

What were some of the challenges you faced? How did you address them?

I believe the first challenge I would say was fighting my internal self. I believe a person can do anything and everything. You just need to say it and work towards it. I wasn’t a top ranked kid in school. I never was. But since I was able to fight my self-doubts and build confidence, I was able to do much better.

Second, nothing is easy. Learning new skills, challenging yourself in a room full of people who know more than you are scary. You need to be mentally prepared for that and accept that. It is part of the process and time will come where you are on the other side of the table. Keep working hard and rest is time.

Third, I had to leave home at a young age of 16. Since then, I have been staying out of my hometown, different states and cities and now different countries. It is very challenging to stay away from parents/family and your loved ones. But this is the harsh reality of the world. Love the people who you grew up with, the people you meet on your way and then people who you settle with.

Fourth, people will doubt you and say you can’t do it. There is no bigger motivation in the world than responding to these people with success. Put all your efforts and do it once, you will love it. And once you do it, you will want to do it again. But remember, you still must love these people. Because they are the ones who will help you grow the most.

Where do you work now? What problems do you solve?

I work at PGIM as a quant. 

PGIM Portfolio Advisory is the multi asset solutions team within the PGIM brand. We work with other PGIM Managers to provide investment support to clients in the insurance and pension funds space. We are responsible for asset allocation and portfolio optimization, and portfolio level hedging. We also work closely with other PGIM managers on security selection, developing economic assumptions and, client reporting.

I work on building complex financial models that help portfolio managers make investment decisions. It’s a very fun job where I get to solve a few of the most complex problems of the world.

What skills are required for your role? How did you acquire the skills?

The skills needed are math, computer science and business/finance. It took me years to acquire all these skills through professional experience and academia. 

What is a typical day like? 

A typical day starts around 7:15 in the morning. I usually meet my team before 8 and discuss the plan for the day and if there are any dependencies or roadblocks others might need me for. On most days, I am either building new models, enhancing the existing ones or doing math to come up with ideas for new models. I usually have a few meetings with the portfolio management team. They usually share ideas based on what they are noticing in the markets and then I try to model those new ideas. 

Majority of my task is to approximate numbers better. The problems in the financial world are so complex that they don’t have one unique solution. My job is to come up with one solution or one approximate solution. Lastly, I also talk to technology teams in case I need some infrastructure related help. This is another important part of my job, even though I can myself do both technology and quant parts, I don’t have to. I have to see what’s more important for my time. So it is very important to delegate responsibilities to others since you can’t do anything and everything in a limited time. I end work around 5:30 and then spend time with my wife and friends.

I love everything about this job. The complexity, the smart people, learning new things every day/week. New challenges every time. Talking to different kinds of people with diverse skillsets. Learning from others and inspiring some on the way.

How does your work benefit society? 

Money management is very important in the world. $100 today is much greater than $100 in 5 years. Money decays and you need to learn to invest. Investing is not just about growing money but it is also about not decaying money.

I help large organizations like insurance companies and pension funds invest money according to their risk appetite. These organizations need to pay insurance claims or provide pension to their retired employees, both of which are very important for any average human being.

The financial system in the world is designed such that the money needs to flow continuously. People need to do all, spend – save and invest for the financial system to work efficiently. We try to help people do the investing part.

Tell us an example of a specific memorable work you did that is very close to you!

This is a tough one. I want to say everything. But one example would be an asset allocation project I did recently. The project was to guide $10B investment for an insurance firm. Since the amount was too big, there was a lot at stake. Which brings a lot of challenges as well as learnings. When someone trusts you with that kind of money, you need to take full responsibility and accountability. You need to make sure your work is error free and have satisfied all requirements. You need to make sure the investment fits the risk appetite of the client and things won’t go the wrong way in extreme situations. Since this money goes to insurance claim payments or retirement incomes, life of normal people is at stake. 

Making sure all of that is taken care of and I am helping many unknown people, makes this one a memorable one.

Your advice to students based on your experience?

  • Don’t be afraid to challenge yourself, take risks early in life. When you are young, you have less to lose.
  • Don’t listen to people who say you can’t do it, instead prove them you can
  • Don’t restrict yourself. You might love something now but in future things might change. Don’t believe that if you have studied XYZ, you can only work in a specific field. I have seen people who did undergrad in ‘history’ becoming quants. Keep exploring and trying new things
  • Work smarter, make sure you know what you want out of something. Put in efforts accordingly.
  • You can’t do everything you want to do, so prioritize what you can. 
  • Be a good person, respect everyone in your journey. Help as many people along the way. Life works in a full circle, you will definitely get benefits out of it
  • Compete with each other but positively, it’s best when everyone around you grows.
  • No shortcuts in life, time is your biggest friend. Everything happens when time comes. So be patient. You can’t become “best” in a day and nobody is born “best”

Future Plans?

Grow deeper as a quant and keep exploring. 😊