Most Important Skills For A Managing Director Quantitative Strategies At O Asset Management
To succeed as a Managing Director of Quantitative Strategies, a strong foundation in "math and coding" is crucial, encompassing linear algebra, probability theory, vector calculus, and statistics; equally important is proficiency in software engineering, including "the use of GitHub," and agile development methodologies. Beyond technical skills, the ability to navigate diverse personalities within a team and communicate effectively with both technical and non-technical audiences are essential for career advancement in this field.
Mathematical Proficiency, Software Engineering, Project Management, Interpersonal Skills, Communication
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
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. Technical skills: Strong foundation in linear algebra, probability theory (including Bayes' theorem), vector calculus, and statistics is crucial. Proficiency in these areas enables understanding of advanced concepts and research papers.
2. Software engineering skills: Develop strong software engineering skills, including different programming styles, collaboration tools like GitHub, and agile development methodologies such as Scrum. This is vital for industry roles.
3. Soft skills: Cultivate strong communication skills (both written and oral), and learn how to effectively work with diverse personalities, especially in a field where individual technical skills vary greatly. This is essential for teamwork and career progression.
Transcript
What skills are most important for a job like yours?
I'd say it's twofold. First, the technical aspect. If I were an undergrad wanting a career in AI and ML, I would focus on math and coding.
From a math perspective, you need linear algebra. You really should understand principal component analysis very well, along with fundamental concepts like basis spaces and subspaces. Take the time in undergrad to master these so you don't have to relearn them after graduating.
You should also study probability theory, going all the way up to Bayes' theorem at least in undergrad. Vector calculus is necessary to understand backpropagation in neural networks, as is statistics for forming smart hypotheses and identifying spurious conclusions.
Don't rush the technical math side. Become as proficient as possible in undergrad. This will give you the fundamental skills to read research papers without being lost, even though keeping up with research in academia is impossible.
On the other side, be as good a software engineer as you can be. I've known computer scientists who are much better software engineers than I am, and gaining those skills would greatly improve my work.
Don't discourage learning better programming styles or collaboration methods, like using GitHub. These are important unless you are certain you want to be a research scientist and pursue a PhD in academia. If that's your path, you might not need to worry as much about coding. However, if you want to work in industry, like me, software engineering is extremely important, just as much as the math. These are the two cornerstone skills for my role.
On the managerial side, Scrum and agile development are very important skills to learn for running software projects. That's what we use in-house at OAM Quant.
Learning to deal with different personalities is also key. In some fields, like fast food, a bad employee can easily be replaced. However, in science, there are individuals who are exceptionally gifted in math and science but may not have easy personalities. Learning to work with diverse personalities, rather than just one type, is crucial for building a high-performing team.
Finally, communication is essential. It's difficult in a technical field like computer science to communicate effectively. Don't neglect this, in both written and oral forms. Anything you can do to improve your communication with non-technical people will greatly benefit your career.