Imperva, a Thales company | Data Scientist for Bot Detection | Hiring 2 candidates | Vancouver, Canada | Hybrid
We are looking for talented, experienced Data Scientists who are ever curious about data problems and eager to write code to get those problems solved.
Members of this team creatively find problems as well as solve them. Much of the work is analyzing detection mechanisms and bot behaviors, making advances in realtime bot detection and mitigation, and implementing those advances in production.
The positions are on the team I lead. The data and problem are interesting, data is intrinsically useful in bot detection, and there is no shortage of challenge to working a difficult problem. Python, SQL, and Rust are some languages we're currently using for most of our work.
Imperva, a Thales company | Data Scientist for Bot Detection | Hiring 2 candidates | Vancouver, Canada | Hybrid
We are looking for talented, experienced Data Scientists who are ever curious about data problems and eager to write code to get those problems solved.
Members of this team creatively find problems as well as solve them. Much of the work is analyzing detection mechanisms and bot behaviors, making advances in realtime bot detection and mitigation, and implementing those advances in production.
The positions are on the team I lead. The data and problem are interesting, data is intrinsically useful in bot detection, and there is no shortage of challenge to working a difficult problem. Python, SQL, and Rust are some languages we're currently using for most of our work.
I'm largely in favor of SSO, but it's not without its downsides, going beyond capital costs: SSO can also be implemented in a way that introduces an onerous latency tax when using services.
Unless you're more specific, I'm going to assume that that "way" is the wrong way.
Initial login shouldn't add more latency than a couple web redirects. The authentication token/assertion should be validated only once and not be needed until it expires or the user logs out.
Before they were acquired by Google, looker had a bunch of really engaged community support people, and the docs were great for what the product did. There were learning paths for different types of users and tutorials linked to references in a way that made sense. I learned a lot about effective documentation (and looker!) from going through what they had.
Even if 100 US-based community support folks were making $300K in total comp, why would you not spend $30MM to keep your $2.6B acquisition humming along? A bit mystifying.
For those unaware, this is a long-form article by the renowned (late) neurologist and writer Oliver Sacks about his personal mental and emotional experiences of what it was like to not feel his leg.
The piece is much more of a New Yorker style than a Paul Graham style. Enjoy it for what it is!
Nearly half of admitted white students at Harvard got in via a VIP lane, which is really high.
But that doesn't necessarily tell the story for what it would be like to apply to Harvard as a white student. There is a subtle yet important distinction in this.
You might have missed a key element from the article itself. There are three versions of the same argument: the first by the author, the second by ChatGPT as an argument in support of the statement:
"I’ve been reluctant to try ChatGPT. Today I got over that reluctance. Now I understand why I was reluctant. The value of 90% of my skills just dropped to $0. The leverage for the remaining 10% went up 1000x. I need to recalibrate."
and the third is ChatGPT arguing against that same statement. As present, it's pretty clear which of the three is the most fun to read.
I really appreciate you saying this, you have no idea how much it means to me.
I spent many evenings and weekends tweaking this asking myself "is this intuitive to someone whose only experience with this topic is everything prior in this post?"
It's important to me that every section is grounded only in all of the previous sections. One of my fundamental beliefs is that anyone can learn anything, provided they're presented the material in the right order.
We are looking for talented, experienced Data Scientists who are ever curious about data problems and eager to write code to get those problems solved.
Members of this team creatively find problems as well as solve them. Much of the work is analyzing detection mechanisms and bot behaviors, making advances in realtime bot detection and mitigation, and implementing those advances in production.
The positions are on the team I lead. The data and problem are interesting, data is intrinsically useful in bot detection, and there is no shortage of challenge to working a difficult problem. Python, SQL, and Rust are some languages we're currently using for most of our work.
Apply here: https://www.imperva.com/company/careers/position/?p=job/ofXg...