New Hedge fund | Full Stack Engineer | On-site (NYC)
We are a new discretionary, tech industry focused hedge fund, founded by a portfolio manager with 15+ years of experience at a Tiger Cub. I lead the data/technology team here and previously spent ~6 years in a similar role at a well known Tiger Cub and briefly running a data startup.
We are hiring for:
- Fullstack engineer for our internal research management web application
On the data side: AWS, Cloudflare, Snowflake, Clickhouse, Dagster
Obviously, we are not the only ones trying to figure out how to use AI in investing but in a world where there are lots of people trying to build picks & shovels, this is an opportunity to be one of the goldminers at a greenfield, new firm. This role makes sense for people who are interested in finance/investing (although you do not necessarily have to have experience in the industry).
We're in full-time in office in NYC, so that's a requirement for FTE.
We work very hard, hours are long, and people are very dedicated to the mission. It is not for everyone, but for those interested in competing in financial markets, love startup atmosphere without near-term existential risk, and want to work with super smart colleagues (both technical and investors!), this could be a great fit.
I write about hedge fund data teams frequently here, if you are just interested in learning about the space: https://magis.substack.com/
Target salary 180-250K, depending on experience + Bonus/Carry (hedge fund equity equivalent)
Contact: I am at: ai (at) nx1 (dot) com (I'm Alex, ai are my initials... you are not being sent to an ai :] )
--
What we're looking for:
- 4-8 YoE in Typescript (we use Vue) + Python experience
- Ability to be in-office in NYC
- Bonus: SQL (ie. you know Window Functions), AWS (ie. you'd know how to configure an IAM role)
New Hedge fund | Sr IC Frontend Engineer | Full-time | On-site (NYC)
We are a new discretionary, tech industry focused L/S hedge fund, founded by a portfolio manager with 15+ years of experience at a well known Tiger Cub. I lead the data/technology team here and previously spent ~6 years in a similar role at that firm as well. We are not the only ones trying to figure out how to use AI in investing but in a world where there are lots of people trying to build picks & shovels, this is an opportunity to be one of the goldminers at a greenfield, new firm. This role makes sense for people who are interested in finance/investing (although you do not necessarily have to have experience in the industry).
We are hiring for a frontend IC engineer for our internal web application.
One of our core advantages is marrying domain experts (ie. experienced fundamental investors from top tier hedge funds) with tech talent. Our frontend (the design, the UX, the UI) _really_ matters to tie the two sides together. The quality of the data we can collect, the speed of testing new ideas, and there ultimately success is driven by the quality of our frontend -- as unintuitive as that might sound.
- We work very hard, hours are long, and people are very dedicated to the mission. It is not for everyone, but for those interested in competing in financial markets, love startup atmosphere without near-term existential risk, and want to work with super smart colleagues (both technical and investors!), this could be a great fit.
- We're in full-time in office in NYC, so that's a requirement for FTE. We are potentially open to exceptional contractors located remotely.
- Target salary 180-250K, depending on experience + Bonus/Carry (hedge fund equity equivalent)
If you are interested, I would love to hear from you:
- Contact: ai at nx1 dotcom , I'm Alex.
- Please include resume or LinkedIn and some example of something you have built (could be Github, could be your current company, anything really)
If you want to learn more about types of things we think about, I will shamelessly direct you to my blog where I write about the space (and did so more while I wasn't at any fund): https://magis.substack.com
New Hedge fund | Sr/Staff Frontend Engineer | On-site (NYC)
We are a new discretionary, tech industry focused hedge fund, founded by a portfolio manager with 15+ years of experience at a Tiger Cub. I lead the data/technology team here and previously spent ~6 years in a similar role at a well known Tiger Cub and briefly running a data startup.
We are hiring for:
- Frontend senior/staff engineer for our internal research management web application
- VP Eng, to lead data engineering + fullstack development (this is a player-coach role, with a lot of IC work expected; it is a small team)
On the data side: AWS, Cloudflare, Snowflake, Clickhouse, Dagster
Obviously, we are not the only ones trying to figure out how to use AI in investing but in a world where there are lots of people trying to build picks & shovels, this is an opportunity to be one of the goldminers at a greenfield, new firm. This role makes sense for people who are interested in finance/investing (although you do not necessarily have to have experience in the industry).
We're in full-time in office in NYC, so that's a requirement for FTE.
We work very hard, hours are long, and people are very dedicated to the mission. It is not for everyone, but for those interested in competing in financial markets, love startup atmosphere without near-term existential risk, and want to work with super smart colleagues (both technical and investors!), this could be a great fit.
I write about hedge fund data teams frequently here, if you are just interested in learning about the space: https://magis.substack.com/
Target salary 180-250K, depending on experience + Bonus/Carry (hedge fund equity equivalent)
Contact: I am at ai dot nx1 dot com (I'm Alex, ai are my initials... you are not being sent to an ai :] )
New Hedge fund | Sr/Staff Frontend Engineer | On-site (NYC)
We are a new discretionary, tech industry focused hedge fund, founded by a portfolio manager with 15+ years of experience at a Tiger Cub. I lead the data/technology team here and previously spent ~6 years in a similar role at a well known Tiger Cub.
We are hiring for a frontend senior/staff engineer for our internal research management web application.
Obviously, we are not the only ones trying to figure out how to use AI in investing but in a world where there are lots of people trying to build picks & shovels, this is an opportunity to be one of the goldminers at a greenfield, new firm. This role makes sense for people who are interested in finance/investing (although you do not necessarily have to have experience in the industry).
We're in full-time in office in NYC, so that's a requirement for FTE. We are potentially open to exceptional contractors located remotely.
We work very hard, hours are long, and people are very dedicated to the mission. It is not for everyone, but for those interested in competing in financial markets, love startup atmosphere without near-term existential risk, and want to work with super smart colleagues (both technical and investors!), this could be a great fit.
Target salary 180-250K, depending on experience + Bonus/Carry (hedge fund equity equivalent)
I work on alternative data in the hedge fund industry. We're not quants -- we don't try to predict the stock market... instead, we try to forecast how individual companies are performing using aggregated clickstream, point-of-sale, and payments data. It's a data cleaning, timeseries, and modeling problem with a lot of domain knowledge necessary.
LLMs can be helpful (Ie. for example, entity resolution for data cleaning) but the core models you have to use to actually make the predictions (this looks a lot more like "old" tabular data approaches).
New Hedge fund | Sr Frontend/Fullstack Tech Lead, AI Engineer | On-site (NYC)
We are a new discretionary, tech industry focused hedge fund, founded by a portfolio manager with 15+ years of experience at a Tiger Cub. I lead the data/technology team here and previously spent ~6 years in a similar role at a well known Tiger Cub.
We are hiring for a frontend (or fullstack) tech lead for our internal research management web application.
We also are hiring talented engineers interested in applying AI to the investment research process, focused on
Obviously, we are not the only ones trying to figure out how to use AI in investing but in a world where there are lots of people trying to build picks & shovels, this is an opportunity to be one of the goldminers at a greenfield, new firm. This role makes sense for people who are interested in finance/investing (although you do not necessarily have to have experience in the industry). You can read a little bit about the type of data work we do here: https://magis.substack.com/p/how-to-do-alt-data-research
Target salary 180-250K, depending on experience + Bonus/Carry (hedge fund equity equivalent)
I think there is a subset of VCs that would pay for this.. unfortunately, that very particular subset of VCs has the smallest budget to pay for things based on their fixed fees/fund sizes.
Firms like CapitalIQ or Pitchbook have their largest contract with giant asset managers for whom a 6- or 7-figure deal would be a very small percentage of AUM (and thereby small percentage of management fees).
For angels/seed stage VCs, you are likely looking at "pro-sumer" like prices. So, something like 100-1000/month at most.
The two other main competitors, aimed at startups (and really venture investors) are Pitchbook and CB Insights.
There are several equivalent products like CapitalIQ (owned by S&P Global), Preqin, and offerings from Factset/Refinitiv that are aimed more at private equity investors (later stage) but also include some startup data.
Finally, there are specialized startup data providers like Harmonic.ai (in depth scraping of stealth startups), G2 (Yelp for enterprise software), or Clay.run (innovative UI) that all specialize in something specific but are not at the scale of the above.
How do they get the data?
The first place is SEC Form D Filings. These are required in the US after private funding rounds (lots of caveats, if, buts, etc. but let's keep it simple). This data alone can give you a decent database to start with. After that, it is web-scraping news articles, news wires, LinkedIn, etc. For very specialized areas (ie. Dev Tools), specialized data sources (say Github Archives) might be useful.
Most importantly, many of these providers aim for give-to-get dynamics. Once they become popular enough, startups will actually seek out having a profile (create data) or fix incorrect data (contribute). This is a great dynamic, of course, because it essentially creates proprietary but free data collection.
Websites like TheOrg.com have done a nice job with org charts -- they take a guess at who you report to... and a lot of employees, annoyed at being "layered", will freely fix the data. If you get enough volume, you create a give-to-get flywheel.
I agree with you what is valuable here is the proprietary data. But, behind that, is the _process_ for creating the proprietary data. You could get very good at web-scraping, parsing esoteric government filings, etc. And, maybe that space can get disrupted by someone better (say with LLMs). But ultimately, if you can get users to contribute data -- that's the "promised land" in DaaS.
I also think UI/interface is not value-less. Companies like Clay.run have done a great job making proprietary data accessible to more users. There is value there -- but the data owner collects a (fair) toll on that.
It sounds like an interesting product but not a fit for us:
- We actually don't want no-code. We want configuration based (or something else that can fit into version control).
- We want very nice, very customizable charts, but don't want to bring our own (our team is Python/SQL and data based; no one writes Javascript)
I have found that both the speed and the frontend control either of these tools gives you is pretty good (with Evidence looking better out-of-the-box just in my personal opinion).
The main problem I always had with the embedded versions of Tableau, Looker, etc. is that they felt super canned (it was obvious it was a poorly/"lightly" white labeled solution -- when you see an embedded Tableau dashboard, you _know_ it is Tableau, etc) and they were slow.
I loved this game and then this style of game (all the Tycoons, SimCity, AoE, etc.).
I did not know it, but it was my first experience with economics. I mistakenly thought that this is what policy planners _actually_ did in the real world. Imagine the disappointment when I found out that was not true.
Nonetheless, it ultimately inspired me to go into data science in market/competitive intelligence. First at hedge funds and now as my own startup.
I have never been able to shake the notion that build a real-time view of the real economy was the most interesting thing to work on.
In general, the M-Competitions (https://forecasters.org/resources/time-series-data/), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have won, generally seem to be variants of older tree-based methods (ie. LightGBM is a favorite).
Will be interesting to see whether the Transformer architecture ends up making real progress here.
It has been very impressive to see what LLMs can do for transforming data into useful structured datasets in Snowflake.
Snowflake Cortex has been critical in this process for open-source and Mistral models. We have also found that using GPT4 and Claude3 to be meaningful within Snowflake. Setting up connections between Snowflake and these LLM providers is not too complex but is annoying, so we've done it once so you don't have to.
The lack of revocability, marginal temporal value, and downstream governance I think makes the prospect of more such data deals happening slim -- or at least, slim without regret.
Cybersyn makes daily trading volumes & prices of all US equities/ETFs executed on the Nasdaq available in your Snowflake instance for free. Data is inclusive of pre-market/after hours activity and is released daily at 6:00am ET. Learn more in Cybersyn Docs:
I have been wondering how to support interactive / real-time web apps based on Snowflake data. I suppose pushing down to DuckDB a subset of data needed for a chart would be one way to do this...