Space Talent Spotlight: Siddhartha Jha

The Space Capital Podcast |

November 26, 2020

“Choosing math as my academic pursuit was very important, especially statistics and computer science. These skills were essential in giving me a foot in the door for my initial career opportunities.”


Space Talent Spotlight: Siddhartha Jha


November 26, 2020


“Choosing math as my academic pursuit was very important, especially statistics and computer science. These skills were essential in giving me a foot in the door for my initial career opportunities.”


Space Talent Spotlight: Siddhartha Jha

Every few years things change and there's no magic set of knowledge that you can rely on. You just have to keep learning.

Siddhartha Jha

A Space Talent Spotlight Series Interview with Siddhartha Jha, CEO of Arbol, former Cross-Commodities Quantitative Strategist at Citadel.

The Space Talent Spotlight is our blog series focused on the leaders and builders at the intersection of space and tech.

Where did you grow up and what is your background?

I was born in India and lived there until I was 11 years old when my family moved to Boston.  I come from an academic family and studied applied math in undergraduate and did my masters in statistics. Following graduation I went to JP Morgan where I was exposed to everything from municipal bonds to swaps, futures, options, treasuries, and macro. This provided me a deep foundation on how markets work, and in particular, how interest rates form the bedrock of all other markets.

After five years, I wanted to do something more entrepreneurial. So I joined a startup hedge fund and I was the first employee for their commodities strategy. I barely knew anything beyond oil and I was looking at markets as diverse as cattle, sugar, and zinc. We didn't have any resources, this would start with blank Excel files and building out everything. Going into commodities was a lot about learning how the world works. There are few markets like it, one day you're watching a particular pipeline issue. Another day, you're watching a coup happening in another country and maybe a cyclone coming in a third country, and trying to understand the ripple effects. I'll never forget trading during the Arab spring and Fukushima, we were up all hours of the night because these markets were so volatile and you had to know every single country's political structure to understand who's vulnerable next. I was there for three years as a senior analyst and we grew from 60 million to over 600 million.I found that there wasn’t much that was intuitive in the market, everything was either too complex or too easy. Due to this, I ended up writing a book on interest rates. 

Next, I had the chance to build my own trading desk and manage my own risk at a shop called Castleton. They're a large firm, but they didn't have any agricultural presence. So I helped build that out. We started with basic presentations on what is corn and soy and how these markets work. I did a deep dive into how these different markets work from wheat, corn, and soybeans to hogs and built out their entire system. 

We began using a lot of interesting data sets and expanded my knowledge of satellite data use for agriculture and all sorts of interesting problems. I got really deep into weather and saw a revolution underway in Ag- and Space Tech. I was an advisor to a group that was trying to launch a radar satellite for agriculture. They were a bit early to that game and being early is often as painful as being late. They pivoted being more data related, but I learned a tremendous amount in that whole experience including the defense and intelligence used to be the main consumers of radar data, but now oil, construction, agricultural companies, all of whom I could see where have these common interests around how they get satellite data into their workflows. There appeared to be an opportunity at the intersection of the financial markets and how you could use satellite data to not just predict crop yields, but also reduce fraud in its agricultural loan portfolio.

Out of all that came Arbol. I always wanted to stitch together this broad background into one idea, a data driven platform that could provide coverage for a persistent issue. From all my experience, we just never had the technology or the coordination ability to stitch together all these little risks. To build a real financial product you need to bring a lot of these people together, you need technology, you need much better data sets than we had available.

What have been your top career accomplishments so far?

I guess accomplishments tend to build on each other. Harvard was a difficult path, not because of the academic side, but because I was in an environment that was challenging to say the least. A lot of my success is due to my parents being incredibly supportive, taking chances and making sure that I did everything to the best of my ability. I went to an inner city high school and faced a lot of the challenges, from drugs and violence, to a whole host of other issues. It wasn't an easy or natural transition going from that environment to Harvard.

The next  big accomplishment for me was making the most of my college experience. And that's why I tried to do the masters and bachelors all combined, trying to really maximize at least the academic aspect, because part of me always felt grateful for being there. 

Another accomplishment was leaving JP Morgan and a comfortable life track to join a startup commodity hedge fund. We got a small office on top of a bank branch in Darien Connecticut, so I switched from a five minute walk commute to a one and a half hour commute, for a firm that had no real history, and I was the first employee. Looking back, it was really scary, I would have been deported if things didn't work out, because I was on a visa. Maybe it's the over optimism of youth, but I thought it would all work out.

And launching Arbol was the culmination of all of this work, something I am very proud of.

Arbol platform (Farmer View)

What were the critical steps/choices that helped you get ahead?

Choosing math as my academic pursuit was very important, especially statistics and computer science. These skills were essential in giving me a foot in the door for my initial career opportunities. My dad always encouraged me to focus on mathematics as a baseline. He helped me read the next year's textbook while we were in the current year. And there were all sorts of assignments given at home, which were much harder than anything we did at school.

What part of your education had the most impact on your career? 

School gives you a very nice foundation, but if you don't build on it, it will often go nowhere because it's just meant to teach you how to think. But the problems you encounter in the real world are so varied that you really need to keep learning because things just keep changing so much with new technologies and techniques, for example machine learning that was so rudimentary when I was in school. A lot of the concepts for machine learning are known from the seventies, but how you implemented and what methods work well in what types of problems that's all being learned, even as we speak similarly in computer science. At this point I can code in nine languages and more than half of them I learned after work in my spare time. Every few years things change and there's no magic set of knowledge that you can rely on. You just have to keep learning.

What about your career have you enjoyed the most and least?

I've always enjoyed learning. Whether it was commodities, the broader markets, or spending my Sunday mornings talking about satellites, learning has been the most rewarding part of my entire career. I’ve also enjoyed managing risk, it's an amazing intellectual challenge to manage a large set of variables that are essentially random. And now with Arbol too, it’s a different set of risks.

I never liked bureaucracy. I understand why they're needed, but in certain times they hobble you. They always start with good intentions, but they become very problematic as the firm grows larger. A lot of work starts to become just maintaining status quo and instead of building new things.

Where do you see the most promising career opportunities in the future?

We've only scratched the surface on what the AI/ML field has to offer. Even now, a lot of what is being done is to solve specific problems using old techniques that are just being put on larger data sets. I think there's going to be an explosion over time with the variety of problems we can solve  with this automation. And it's related to having computers start to understand the world even in a better way than we do now. It's a wide ecosystem, but I think there's a lot of career opportunities that we haven't even thought of yet in those fields. 

To be qualified for these opportunities, statistics and computer science are going to be the core skills. I see overlap with computer science and everything in the physical world. This technology has barely scratched the surface in the space sector, especially with regards to satellites.

What advice/resources would you share with the next generation?

When you're in school, there's this almost a hidden pressure to pick your path in life right then. And I think that you have to be very, very flexible, especially as we go forward and people don't have the same job for 10, 20, 30 years. You have to be constantly learning and sometimes reinventing yourself every so often to take advantage of new opportunities. What are the new technologies coming out? What are the new understandings that people are developing and how do you take advantage of that? Or how do you contribute to something big and meaningful? The notion that got a degree and I'm done with school no longer exists. Talking to people who have done interesting things is always a good place to start.

Is there anything else you would like to share?

It's extremely important to find people whose careers and lives you admire. I've been lucky to have people who always looked out for me and advised me since my high school days. This has been important at all stages of my career, I had people whose experiences helped me avoid a lot of mistakes. Even if someone has even 10 years more experience, they can make a vast amount of difference and they might just save you from stumbling. Sometimes it's not a formal mentor relationship, just someone that is more experienced than you in your field. It’s helpful to learn how people make decisions and how they think about challenging problems.


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Space Talent Spotlight: Siddhartha Jha