Main Responsibilities of a Managing Director, Quantitative Strategies at O Asset Management
Trevor's Managing Director role at O Asset Management involves a blend of leadership, managing a team using Scrum and Jira, and technical work, including coding (30-60% of their time), research, and analysis. A crucial aspect of the position is effectively communicating complex technical results to non-technical stakeholders, requiring the ability to "meet them halfway from a communication perspective" and justify resource allocation decisions.
Leadership and Management, Artificial Intelligence (AI), Software Engineering, Data Analysis and Interpretation, Technical Communication
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
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Job Functions
Traits
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 a team of engineers and scientists, utilizing Scrum and Jira for project management.
2. Continuously learning and adapting to the rapidly evolving field of AI and machine learning by reading research papers and writing code.
3. Effectively communicating technical results and strategies to non-technical stakeholders to secure funding and support for projects. This includes creating documentation, roadmaps, and presentations
Transcript
What are your main responsibilities within your current role?
My main responsibilities can be understood in two categories: leadership and technical. From a leadership perspective, I manage and define the work for my group over the next quarter or six months.
I also ensure that our engineers and scientists are satisfied with their work and remain challenged. The focus is on building a high-functioning team.
For those looking to manage large-scale AI projects, I recommend learning the concepts of Scrum. We specifically use Jira to execute our Scrum process. This is essential for anyone aiming to lead such teams.
It's also about deciding where to allocate resources after reviewing research. With new advancements daily, defining this direction is crucial. You need to align these decisions with the company's vision.
On the technical side, which is about half my time, I spend 30% to 60% writing code. Another 30% is dedicated to reading research papers and analyzing results.
Staying current in machine learning and AI is vital, as the field moves incredibly fast. If you seek a career where you learn something once, this is not the field for you.
However, if you thrive on continuous learning and being pushed, this field might be a good fit. Writing good code is essential, unless you're focused solely on theoretical research. You'll need to implement and test new ideas, documenting results with minimal bugs and ensuring reproducibility.
Many AI scientists and engineers I meet don't sufficiently value writing good code, yet it's a critical part of my job. Documentation is also key. I often create versioning documents so others can reproduce my work.
I also build presentations to communicate our team's accomplishments effectively. Additionally, I develop roadmap documents outlining our past, present, and future direction. This helps justify our work to stakeholders who may not have deep technical knowledge.
You must bridge this gap by communicating in a way they can understand. Simply citing technical details isn't enough. You need to explain the performance improvements and the problems you're solving.
My daily work involves both leadership and technical responsibilities. It's not just about reading papers and writing code, but also continuously documenting and communicating effectively, which is a challenging but essential part of the job.