Hi,
Hope this topic does not violate any rules. For me, I finished a part-time CS degree last May and tried to switch my career to a data analytics position (turn 31 this year, so, kind of looking to switch asap if possible, as the difficulty will only increase after time passes). For the forum, I think it is pretty interesting and I will enter my 9th round with staked NMR.
For sure, the project is interesting as it improved the skillsets while I can earn a little extra. I am thinking about how to turn my project experience here to be a presentable project to put on my resume? Clean up the process I have and load it into Github. But since we are only predict the 0.25, 0.5 thing in the target (which means I cannot explain what I predict), do you think it helps to put on resume? Any thoughts?
Thank you.
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I’m also a student. On my resume I talk about the libraries I use and the different ML techniques I’m trying.
I’m just spitballing here but if you keep submitting it for ~20 of rounds you can include your place on the leaderboard and if you stake at all you can say your ROI. Something like "I’m so good at Machine Learning that a hedge fund paid me N dollars to use my predictions to bet on the stock market {link to your numerai leaderboard profile}.
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I’d definitely put Numerai on your resume. If you haven’t already got a job in the area I think data science competitions are the best way to stand out. I think personal projects (e.g. you read a paper, have an idea, try it, and document it) also do have their merits, but it doesn’t compare your ability to other people. Whereas in competitions like this you can express your ability as a number on your resume (your percentile rank on the leaderboard), which I think is a great way to summarise your expertise. The big problem with a lot of data science competitions is you’re not productionising your work, whereas in Numerai you’re staking money on your models and submitting them every week, so I think performing well in this kind of competition has a lot more weight for your ability. I think Numer.ai is great for your resume as given the tournament never ends you have a lot of time to try out novel ideas and try out advanced techniques (e.g. custom loss functions and training loops). If you can come up with a novel idea that works, or implement an advanced technique, then it’ll look great on your resume. I wouldn’t worry too much about GitHub as most recruiters don’t have a lot of time to look at that stuff in much detail. But if you can put your code alongside a report explaining what you did and why on your GitHub, then that’s definitely bonus points.
Although they don’t tell us exactly what the target is, if you watch the videos on their YouTube you can get a rough idea of what we’re predicting. Each week our models predict the ranks of global equities 4 weeks from now, and we’re scored on the correlation of our predicted ranks with the actual ranks at the end of those 4 weeks. But what those ranks actually correspond to I don’t think we know. So in brief I’d just say we’re ranking global equities.
My 2 cents:
I often review CVs and do down-selection for Junior/Graduate Data Science roles. We will often have huge numbers of applications (100+ is normal) for a single role, but we’ve hired a couple of career changers in junior/apprentice roles.
Some general advice:
- I’m normally impressed when I see 1-2 examples of data science projects in addition to standard CV things. (e.g., ‘I regularly complete in online machine-learning competitions. For example, I compete on Numerai (include a link to your profile) where I have a XGboost model which does well…)’.
- Keep it short.
- Provide evidence if possible.
- For the top candidates I will check evidence (GitHub, Kaggle profiles). But I won’t spend much time there, just read some code and get a gauge of their competence. Messy code will probably do you more harm that no code. I’ve had people claim Kaggle experience prominently on their CV, for them to have no medals and simply a Kaggle account - don’t do this!
- Include a sentence or two at the top of your CV explaining your career pivot. Something interesting here will help you get an interview.
- Lists of ML libraries (while needed) don’t impress me much. Everyone seems to list EVERYTHING they can think of. A couple of key libraries with some ‘theme’ (the pyData stack, xgboost, sklearn, jupyter), which you can explain in interview will probably impress me more.
TLDR;
Yes, include Numerai experience, but I would focus on explaining succinctly what it is and what you do on it.
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@mrquantsalot Thanks for sharing the thoughts. Yeah, I am also thinking about how to show some results, something like ROI maybe. I am also trying to build up the positions in the leaderboard.
@ml_is_lyf Thank you for sharing the thoughts. Yes, you actually point out the puzzles I try to solve. As currently, my work is more dealing with excels (with a little R and SQL). I am exploring the best way to build up some projects and try to compete in some competitions. Yeah, I think I should better document my approach and explain it (as current, I just do some predictions in google colab). In addition, will definitely try to find the videos you mentioned, as it sounds like a pretty good way to tell a better story.
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@sirmobius Thanks for sharing the thoughts. Yeah, have to say it is very competitive pools. Guess will for sure to try to include it. Because I also tried Kaggle, the best one is a silver medal and try to compete more now (as Kaggle’s competition is a little easier to show the impact). Guess will pay more attention on the code quality as well to make it more clean.