Quant vs roles in tech?

Amazon decidenow1
Jun 26 9 Comments

I worked at both Amazon and Bloomberg as SDE and I have lots of passion and experiences with machine learning (so basically I’m MLE).

I’m strong and interested in both engineering/CS and math/stats, and my career goal is certainly to bring these two together.

So for career development, I could choose continue in tech, or becomes quant at financial industry.

Continue in tech: I’m familiar with everything (work culture, skills, interview tips ), and no further input, and I could be either MLE or data scientist.

Switch to quant: Get more different life experiences, and I’ll gain lots of financial domain knowledge (lacks in tech, and I think domain knowledge and soft skill can make a difference for promotion in long-run). Also pay upper limit will be much higher at top funds?

But this is whole new field, means More input such as getting a new MFE degree? Also heard quite competitive and more stress ?

Thx for any suggestion!

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TOP 9 Comments
  • Bloomberg / Eng 7bd92"
    How did you become MLE? I'm trying to transition into an ML role at Bloomberg, having completed the internal ML 101 training.
    Jul 3 7
    • Bloomberg / Eng 7bd92"
      It's the lecture series you saw on YouTube, plus more lectures and a bunch of HW assignments. In the last HW, you build an MLP regression (and optionally MLP classification) neural net from scratch. I'd estimate that less than 15% of ENG teams work on ML, and there seem to be more people interested than there are ML-focused openings
      Jul 3
    • Google sQNX36
      Thanks! So it’s competitive to get into ML team or projects within BBG? Hmmm,but I do feel suspicious the power of ML in real financial industry. This could be different from tech industry
      Jul 3
    • Bloomberg / Eng 7bd92"
      Basically, it's Bloomberg's job to feed our Terminal customers raw data asap and reliably. Performing predictive analytics on it is more the prerogative of a financial firm that actually trades (e.g. a hedge fund, or an algorithmic trading division of a bank). There are also legal implications around making predictive trading recommendations to our customers, since such recommendations can move markets. Thus, I'm beginning to wonder how many uses there actually are for ML in the Bloomberg Terminal, aside from derivatives pricing and a few other use cases that probably adhere to standardized models anyway. Bloomberg Law and Bloomberg News are another story...there is ample opportunity for NLP data mining and analytics in those departments.

      What percentage of Google ENG teams would you estimate to have machine learning projects in their roadmap (excluding backlog items)?
      Jul 3
    • Google sQNX36
      Well Google is huge. So in terms of percentage I don’t think they’ll be high. But definitely Google is leader in AI and ML, and numerous training and resources inside google for this. As for myself, only problem is I think we need good domain for applying ML, finance is good one.
      Jul 4
    • Bloomberg / Eng 7bd92"
      Yeah, that's what my manager tells me too. "Figure out how to apply ML in our team." But our team is a content rendering team, so obviously there's not much scope for that
      Jul 5
  • Google sQNX36
    curious too
    Jul 3 0