A Day In The Life Of A Managing Director Of Quantitative Strategies At O'Donnell Asset Management
A Managing Director in quantitative strategies at a small asset management firm spends their day deeply involved in technical work, "keeping the server running" for deep learning experiments and collaborating with scientists and engineers to improve algorithmic trading strategies. This involves continuous learning from academic journals ("C B P R I C M L I C L R, uh, these are all fabulous journals") and the open-source community to inform strategy development and staying on top of the field of finance.
Quantitative Finance, Algorithmic Trading, Deep Learning, Software Engineering, Leadership in Tech
Advizer Information
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Job Title
Company
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Trevor Richardson
Managing Director, Quantitative Strategies
O Asset Management
Arizona State University
M.S. Computer Science at Arizona State University
Engineering - Industrial
Finance (Banking, Fintech, Investing), Technology
Data and Analytics
Honors Student, Scholarship Recipient, Took Out Loans, Worked 20+ Hours in School
Video Highlights
1. Managing directors in quantitative strategies at small startups have significant responsibilities, including overseeing server operations crucial for running deep learning experiments and ensuring the continuous improvement of automated algorithmic processes.
2. A key aspect of the role involves collaborating with scientists and engineers, coordinating efforts, and leveraging expertise in deep learning to enhance portfolio management strategies.
3. Staying current with advancements in the field is vital, necessitating continuous learning through reading research papers from journals (e.g., CVPR, ICLR) and industry publications, and documenting insights in shared notebooks for team knowledge sharing and future strategy development.
Transcript
What does a day in the life of a managing director in quantitative strategies look like?
It's a very fast-paced life. I work for an amazing small startup called O'Donnell Asset Management. We are a small group with huge aspirations, aimed at trying to become profitable.
When you're an engineer or a scientist in a technical or leadership role at a small startup, you're carrying a lot of weight and responsibility. It's fun. Everyone else around you is fired up, and you usually have a large impact on the culture.
My day typically starts with my Jira tasks. I review my commitments to the team and project on a monthly cadence, then I remind myself what really needs to get done today. I usually connect with two or three other scientists or engineers on my team to coordinate our work.
One of my most important responsibilities is keeping the server running. When running large deep learning experiments, we use a large GPU machine. We have an internal server with multiple GPUs at our headquarters. When that server isn't running, it means we're missing opportunities to ask questions, and we're losing money.
So, every day I check on the server to make sure we can upgrade the strategies we've built. By "strategy," I mean a fully automated algorithmic process that perceives world events and then attempts to rebalance a portfolio to a desirable state.
Every day we look to improve our production code. To do this, we develop hypotheses, test them, and evaluate them on our servers. Keeping that server running and checking in with other scientists to ensure their work connects with server experimentation is crucial.
My day usually ends with a period focused on learning and looking ahead rather than just implementing new code features. After getting the server running and checking in with scientists, if anything looks promising, we move it to production. However, we often don't find significant improvements, so we must continue thinking about the future.
This future thinking involves reading open-source literature from the deep learning community. Publications like C B P R, I C M L, and I C L R are valuable resources. We also read finance journals to understand trends in pairs trading, backtesting, and evaluation. Marrying these two worlds is a very important part of the job.
As scientists, we use notebooks to document our findings so we don't forget them. When I'm reading literature, I take notes on what might be valuable for our organization and strategies.
Occasionally, I have more engaging tasks, like onboarding new employees or recruiting new hires. I enjoy bringing talented individuals onto the team. I also connect with our leadership group to set the vision for our strategies and discuss which AI technologies to focus on. Meeting with outside clients is also enjoyable.
However, most of my job remains technical. My managerial role is primarily about interacting with other scientists and engineers, not dictating their work. I've found that this approach works better with scientists.