They may not want to disclose the project you're going to work
Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
41–50 of 116 posts
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#42I wish companies didn't think their interviews were some weird "secret sauce" -- I've had companies flat out refuse to tell me what the interview process would be like, or even how long I should expect for the interview beyond "1-4 hours". Well, you know 1 hour is a lot different than 4 hours! Personally I think all companies should be using pair-programming or contract to hire, as I think trivia questions and whiteb…
"Trivia questions" are not useless. I've put out job ads only to receive hundreds of applications from all over the world, many not even in English or without even a word accompanying a CV. Of the "proper" applicants, I've then had to deal with dozens of candidates that claimed a competence they did not have. And guess what - that's a lot of effort and time which is taken away from the business. In my case, that's a…
Have "actual machine learning" experience, no idea what "high leverage" means.
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#43Earlier quoted context omitted.
"Trivia questions" are not useless. I've put out job ads only to receive hundreds of applications from all over the world, many not even in English or without even a word accompanying a CV. Of the "proper" applicants, I've then had to deal with dozens of candidates that claimed a competence they did not have. And guess what - that's a lot of effort and time which is taken away from the business. In my case, that's a…
Have "actual machine learning" experience, no idea what "high leverage" means.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer?
For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same guy whose CV showed 4 years in a research lab doing stats [2]. A lot of people are just going through the motions.
[1] https://en.wikipedia.org/wiki/Leverage_(statistics)
[2] reposting for fun: http://4.bp.blogspot.com/-IJo_Tkw95-o/VANnTdCFgHI/AAAAAAAADV...
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#44Earlier quoted context omitted.
Have "actual machine learning" experience, no idea what "high leverage" means.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer? For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same…
Yeah... Have actual machine learning experience as well, never heard the term leverage as applied here. Sometimes the nomenclature can confuse people, and I guess the original point about more in depth interviewing processes was related to discerning whether the fundamental knowledge and team fit are there, not whether the candidate is aware of the particular subset of CS trivia the interviewer is currently interested in. I say this while admiting that I've made this mistake in the past while interviewing candidates for CS positions...
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#45Earlier quoted context omitted.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer? For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same…
Yeah... Have actual machine learning experience as well, never heard the term leverage as applied here. Sometimes the nomenclature can confuse people, and I guess the original point about more in depth interviewing processes was related to discerning whether the fundamental knowledge and team fit are there, not whether the candidate is aware of the particular subset of CS trivia the interviewer is currently intereste…
What do you call high leverage observations then?
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#46Earlier quoted context omitted.
Have "actual machine learning" experience, no idea what "high leverage" means.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer? For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same…
Ah we usually call them outliers and use robust techniques or not-L2 stats when they are pervasive in the data. Still, in my experience, handling outliers is an application dependent thing, as they may be benign or they may suggest another stat model entirely.
Otoh, I agree not being aware of over fitting is definitely grounds for setting aside a candidate for ML job.
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#47Earlier quoted context omitted.
Have "actual machine learning" experience, no idea what "high leverage" means.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer? For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same…
Interesting, I guess Leverage is one of those terms that one might not encounter frequently except in some specific situations
I don't claim to be an expert but at least I know what Moment is
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#48I wish companies didn't think their interviews were some weird "secret sauce" -- I've had companies flat out refuse to tell me what the interview process would be like, or even how long I should expect for the interview beyond "1-4 hours". Well, you know 1 hour is a lot different than 4 hours! Personally I think all companies should be using pair-programming or contract to hire, as I think trivia questions and whiteb…
I think a part of the secrecy is so that if it goes poorly they can stop the interview after a really bad session without indicating to the candidate that it's gone so badly. "Yup, it's always just two sessions, goodbye." This is an actual rational I've heard. I don't agree with it, but it's a thing.
I'd rather wish for being open, honest and direct with the rejection then tolerant and polite. It saves a lot of time and emotional investment.
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#49Earlier quoted context omitted.
I'm referring to leverage in statistics [1], and particularly how high leverage observations may affect your model and how people deal with them. Clearer? For context: one of the candidates I interviewed told me that the best way to pick a model was to pick the model that would have the highest R squared when fitted to the whole dataset. I asked him about overfitting and he didn't know what I was talking about. Same…
Ah we usually call them outliers and use robust techniques or not-L2 stats when they are pervasive in the data. Still, in my experience, handling outliers is an application dependent thing, as they may be benign or they may suggest another stat model entirely. Otoh, I agree not being aware of over fitting is definitely grounds for setting aside a candidate for ML job.
There's a difference. Imagine a simple one dimensional linear regression.
A value with x close to the mean, but extremely high y would barely move the line as a whole up or down as you vary y. The gradient would not be much affected.
A value with extremely high x does not need to move much to change the gradient of the line. You might even change the sign of the gradient.
Both are outliers but the second has higher leverage than the first.
Edit: illustration: http://imgur.com/vVKwjHh (right hand side are high leverage)
Re: Ask HN: Can the “Who is Hiring?” post include a bit about the interview process?
#50Earlier quoted context omitted.
Apple not big enough?
Maybe it's because they aren't as focused on software as the others
Yes but at least they are a tech company. Facebook has done some cool stuff in the back-end and released some great open source, but they are basically a single-product company, and its a consumer facing website that sells advertising. Assuming that everyone reading HN would rank them above Apple tells you quite a lot, really.