I am stuck

So for some background on myself. I graduated math and computer science from Cornell in 2018, with a focus on numerical methods. I have since worked both at SIG and Brevan Howard in finance. I was at SIG fulltime as a front office developer on US Equity Options Desk before being let go after 18 months, and at Brevan Howard I worked as a contractor building out supply and demand balance for Natural Gas.

At each point in time, I feel like I dont really perform very well. I generally view myself better with things like contest math, applied math, and pure math than coding, but I have gotten better at it especially in python and C++ to the level I get interviews, but I cannot do it well enough as some other people (people who went to Chinese Universities and went to graduate school at the likes of MIT etc). I have since tried to move to more of a quant role, which relies on stochastic calculus, high-dimensional statistics etc, but I feel like I am at disadvantage really only having a bachelors degree. I am also too theoretical for trading role. 

The question is: "what should I do"? Should I go to tech to do machine learning and leave finance behind? Should I try to get into trading role? How does one go about that? 

5 Comments
 

Based on the most helpful WSO content, here’s a breakdown of your situation and potential paths forward:

  1. Transitioning to Tech (Machine Learning/AI):

    • If you’re considering leaving finance, transitioning to tech could be a viable option. Your background in applied math, numerical methods, and programming (Python, C++) aligns well with roles in machine learning, data science, or AI.
    • To strengthen your profile, consider:
      • Upskilling: Take specialized courses in machine learning or AI. For example, Coursera offers "Machine Learning for Trading" and other relevant courses.
      • Projects: Build a portfolio showcasing your skills in machine learning, such as predictive modeling or natural language processing.
      • Networking: Leverage LinkedIn and alumni networks to connect with professionals in tech roles. Highlight your quantitative background and problem-solving skills.
  2. Pursuing a Quant Role:

    • If you want to stay in finance but move into a quant role, you’ll need to address the educational gap. Many quant roles prefer candidates with advanced degrees (Master’s or PhD) in fields like Data Science, Computer Science, or Statistics.
    • Steps to consider:
      • Self-Study: Teach yourself coding in Python and R, as well as stochastic calculus and high-dimensional statistics. Free resources and tutorials are widely available online.
      • Bootcamps: Short-term data science or quant bootcamps can help you gain practical skills and make your profile more competitive.
      • Advanced Degree: If feasible, pursue a Master’s or PhD in a quantitative field. Programs like MIT’s MFin are highly quantitative and could bridge the gap between your theoretical knowledge and practical application.
  3. Trading Roles:

    • If you’re interested in trading but feel “too theoretical,” consider roles that blend your math expertise with trading, such as quantitative trading or algorithmic trading.
    • To break into trading:
      • Learn the Basics: Familiarize yourself with trading strategies, market dynamics, and risk management.
      • Coding for Trading: Enhance your ability to translate mathematical models into code. Employers value candidates who can implement algorithms efficiently.
      • Networking: Reach out to professionals in trading roles to understand the skills and mindset required. Platforms like WSO can be a great resource for advice and connections.
  4. General Advice:

    • Focus on Your Strengths: Your strong foundation in math and numerical methods is a unique asset. Highlight this in interviews and applications.
    • Imposter Syndrome: It’s common to feel outperformed by others, especially in competitive fields. Focus on continuous improvement rather than comparison.
    • Mentorship: Seek mentors who can guide you in your career transition. Many successful professionals are willing to share their insights and experiences.

Ultimately, the decision depends on your interests and long-term goals. If you’re passionate about finance, doubling down on quant skills and pursuing advanced education could be worthwhile. If you’re drawn to tech, machine learning offers exciting opportunities to leverage your math and coding skills.

Sources: Q&A: Quantitative Analyst - Machine Learning, Analytics, & Quantitative Research/Investing, Banking to Software Engineering? All advice appreciated., https://www.wallstreetoasis.com/forum/trading/move-to-tech-in-search-of-better-wlb?customgpt=1, Prop Trading (MM) v.s. BB Sales and Trading, 5 things I wish I had known when I was a business undergrad...

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 

Sorry to hear that, bump to hear any of the senior guys on here. I’m in a similar position but non-technical. I've been watching this forum like a hawk for days.

 

With limited information here, I don't think I'm hearing a strong case to go into trading. Traders are decisive. They also are very good at dealing with uncertainty. And generally, they like trading, and they know they like it. So your story is you're kind of a developer but you don't think you're that good or interested in it, kind of a quant but you also don't think you can compete there, and you want us to tell you want to do. Ok, find something you're interested in, and get really good at it. Be the best quant or developer, or even trader or researcher, at some niche area that's interesting. Someone may have gone to MIT, but if you spent 5 years modeling something, and know the ins and outs of it, who are firms going to hire? Don't try to be everything, try to be something. Good luck.

 

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