data4lyfe

Most people likely cannot quit a high paying job when their identity is also wrapped in how much they’re earning. I see this a lot from all of the newly minted AI millionaires.

You really have to just ask dumb interview questions. Testing them on answering questions while putting their hand over their face or their hands covering their eyes now. It's really dumbi-fied our interview processes (see https://datastream.substack.com/p/my-foolproof-interview-que...)

Relevant anecdote from Bill Gurley's talk: https://youtu.be/7PkWc-IDTHk?si=p8AUBHqXa76HuA6A&t=3228

"If the the average CIO is committing 10 or 15% of their budget to AI. If you're not in that you're getting shrunk."

Wow that's terrible

The man is an absolute legend. And the most awesome thing about poker is that you can play until the end of your days if you can stomach the swings.

I don't think this would work as well in cities like San Francisco and Seattle....

The fact that U.S. News and World Report can pull a school from it's own rankings and it makes such a strong impact is really a strange phenomenon.

The role is called data engineer now. It's just one of many data engineering roles. Data engineers at a non-tech company could be 1 person holding up the entire system doing administrative tasks on it so to speak. It could also be one of many persons that work towards holding up Youtube's ML recommender systems.

I think just in general data engineer is a better term to find the same role across a lot of companies today.

Yeah we're gathering a lot of data on the career process!

Hi - I'm the CEO of Interview Query and we wanted to showcase our new job board built exclusively for data scientists and related fields. We've curated jobs specific to data science, analytics, machine learning engineering, and data engineering.

Each job was scraped and then classified into a specific data science sub-group. We've also added filters for seniority, company, city, and more. We additionally added links to some job postings that have company interview guides that are on our site.

Hopefully this helps data scientists and alike find jobs that are relevant to their future job search!

1. Technical interview prep companies give structured learning and practice to people that aren't good with structure.

2. Algorithm technical interviews provide a structure for companies to compare skill levels of different candidates against each other

3. Neither is the best scenario for either party but in a chaotic world without structure, each one is trying the best they can towards filling a job role by meeting with someone in a span of 45 minutes.

Personally as a founder of an interview prep company for data scientist (https://www.interviewquery.com/), I find that we try to just teach candidates concepts through bite-sized problems and repetition. Some people might hate it, but we're essentially playing the game that the companies are holding up. So you might as well learn to get good at it.

Exactly. Let's say that MarketRank grows in market share to be the dominant search engine. What's to stop someone from then gaming Reddit / HackerNews posts and comments with bots and fake accounts because the operator understands that these websites now have "community verified rank importance"?

Is there a way to calculate exactly how much this affects the every day person? Specifically what I'm looking for is a table breaking down the costs by category.

For example - I don't really drive that much on a day to day basis compared to the average American, and so if it's a huge 50% increase in gas prices yoy, that over indexes the inflation number for me because I might drive 80% less than the average American. So I would assume that inflation is really <7.9% for me.

I think U.S. News Ranking only really has merit because it's been around for a long time and has also done a good job of transferring its brand into good SEO.

Originally a side project two years ago, now I'm full time on it: https://www.interviewquery.com/

We help data scientists land jobs by being the Leetcode for data science.

It's incredibly easy to be a niche influencer nowadays. If I write a blog post on a niche subject that I know a lot about, I can easily take that blog post, re-word it a bit, and record it for Youtube with some light edits -> example data science account: https://www.youtube.com/channel/UCcQx1UnmorvmSEZef4X7-6g

Youtube recommendation algorithm is so good at rewarding continuous creators. The difficulty is that the effort in making videos is surprisingly high and scaling is hard.

The garbage in garbage out cascading failure generally seems to crash pretty fast. Given the U.S. is a capitalistic society the companies / institutions that do this and don't achieve their goals through data science should be apparent and then fail accordingly.

Am I missing something here?

Best optimization of weather conditions has to be seasonal. Seattle and Portland may be horrible 8 months out of the year but the summer months are quite perfect.

What I want to know are which cities are great seasonal cities in which I could potentially buy a condo for the high seasons, and then leave in the non-high seasons for a potential rental at a still competitive price.

For example: SF sucks in the summer, but maybe the tourists don't know that.

Redwood City*