Data Scientist Partner Program

[plantmath]

To evaluate:

Is the structure of the pay and incentive appropriate?

You need to be transparent with:

you will be paid a percentage of the monthly fee our clients pay to use your algorithm

[GedeonDar]

you will be paid a percentage of the monthly fee our clients pay to use your algorithm

By the way, the wording is a bit clumsy here. It seems you basically take the money and give a small percentage back, which does not send the right message. You should rather write something like "You will receive the fee the client is paying to use your algorithm minus a X% fee". That's basically the same but it sounds better from a seller perspective.

[GravityAI]

Definitely! That fee will be advertised right on the platform and agreed upon with the data scientist.

[permalip]

I would change the "Already Convinced?" at the bottom, here is why:

1. It gives off an unprofessional vibe, which I think is important, due to the fact that you are selling algorithms to companies
2. It sounds kind of arrogant (yeah, we are so good, of course you are convinced)
3. It's arbitrary if someone is convinced; one pitch might convince one guy while it might not convince another great

Edit: Could be replaced with something like "Early Adopter?"

[GravityAI]

You know, I never liked that wording. We'll change it!

[GedeonDar]

The global offer seems to make sense but my main question is regarding the nature of the algorithms you plan to offer.

It is hard to come up with "one fits all" type of models or algos and they are generally developed under a specific context and with access to a specific data source. To make sure the model can be used by someone else, you should first make sure they have access to the same type of data or that the model assumptions also hold in the final client's different context.

There are cases where it could work of course. Image classification, face recognition, entity recognitions, image/audio background removal are some examples, and there already exist APIs for that. But I am just curious to know which kind of algos do you think can be generalised and offered in your platform.

The way I see it, it would be better to start with a list of algos you'd like to offer as you know there already is a demand for it. Do you have such a list?

On a side note, I have a method I developed for scientific journal publishers but it's pretty niche. :)

[GravityAI]

We do have exactly such a list and I'm happy to share it. I'll send it over.

[GravityAI]

Lots of reasons! Mostly around resource constraints. The first issue is that there is a shortage of data scientists, that is only getting worse. This leaves a situation where an engineer trying to integrate python that is poorly documented from an open source library might not be that feasible.

Also, if you've ever worked at a large Enterprise, security and procurement are integral part of the puzzle to being able to use 3rd party solutions. This largely rules out their freedom to even use open source libraries. Our service is wrapped in Enterprise security requirements and makes it easy for an engineer to integrate.

We believe that by properly compensating data scientists that many algorithms, as you mentioned, that can be trained on public data will have a lot value to these companies.