Hi HN!Today we're launching TermScout (https://termscout.com), a software contract rating platform. We developed a contract abstraction and scoring algorithm that enables apples-to-apples comparisons of software contracts. Our initial database has 150 public contracts. These are typically the click-through customer agreements for cloud and SaaS providers that many of you likely regularly sign....
Location: United States (Denver)
Remote: Yes
Willing to relocate: No
Technologies: Python (Django, pandas, numpy, sklearn, xgboost), Linux, DevOps(GCP, Kubernetes, Docker, Helm, Terraform), SQL
Résumé/CV: https://drive.google.com/file/d/1Qplz4HQEYyFb0ItKODPN2FleGjHQIrEK/
Email: [email protected]
For the last eight months I've been CTO and co-founder at a startup I founded with a few co-workers after the last startup we were all working at shut down due to impacts from COVID. We released an iOS and Android app in September, but I am looking to move back into an employee role where I can focus on solving technical problems rather than running a startup.
Prior to my startup I was the second employee at another startup creating enterprise analytics software. I spent multiple years working in a hybrid backend engineering, data science, and devops role where I developed machine learning models and REST APIs and deployed them on Kubernetes using SQL and noSQL databases on GCP. I was heavily involved in the full implementation cycle from ideation, development, testing, deployment, and maintenance.
I am looking for a full time backend or data science role (ideally a bit of both!) in Denver or Remote. I have been fully remote the last 8 months and discovered I work very well in that environment.
I went through the first phase of the course as an intro to AI/DL and thought it was really great from a high-level perspective. If you have a decent understanding of Python you'll have a working model running on AWS within the first few hours of the course which is very rewarding.
It does a better job than I expected explaining the underlying intuition of the math, but doesn't dive deep into the actual formulas. There are obviously tradeoffs to this approach and if you want to continue in the field you'll need to do something to fill in this background, but as far getting your hands dirty and understanding the basics I really liked the fast.ai approach.
I felt similar to you when I first started learning ML but their code first approach really helped it click for me on an intuitive level. Then you can go back and dig into the maths behind it.
A similar issue was just discussed yesterday in a thread on Jeff Bezos' letter to shareholders:
"A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp."