Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

A Student's Guide to Preparing for Data Science Interviews

acheronanalytics.com

Post reply on HN
Print view View original

31–40 of 61 posts

Re: A Student's Guide to Preparing for Data Science Interviews

#31

Post by mindcrime »

softawre wrote: post #30
mindcrime wrote: post #26

Earlier quoted context omitted.

spoopy01 nailed it. Most of the people hiring data scientists don't really know what data science is, or probably even why they need it. So they inflate the hell out of the requirements to a. CYA and b. hopefully get somebody so experienced they can come in and make up for the lack of organizational understanding of data science. IOW, they want somebody who can "teach us what we don't know".

Teaching me what I don't know is what I want with all of my engineering hires. I want people better than me who will tell me why my architecture isn't ideal or whatever the case is.

Yes, but I'd argue that's on a different level from "teach us why we need an 'architecture'" or "teach us how to use this 'data science' stuff". Some people are trying to be buzzword compliant when they don't actually understand the buzzwords. Ya know?

Or maybe there's a better explanation for asking for a candidate with a Ph.D. in Statistics to create a linear regression model in Excel. Because truth is, for many companies that's all they need.

Re: A Student's Guide to Preparing for Data Science Interviews

#32

Post by FlyingLawnmower »

minimaxir wrote: post #10
cbcoutinho wrote: post #5

Earlier quoted context omitted.

If you had to summarize into three points?

I have one notable observation: On Hacker News, every time an interview thread pops up, there is a discussion decrying the use of technical screenings before an onsite, and often suggest practical work experience instead using a homework assignment (which this article does not discuss). Most of the companies I've talked with for data analyst/science roles have given me both a homework assignment and a technical scree…

Did you find another job yet? If not, what type of opportunity are you looking for? Data science is also a wide, somewhat poorly defined domain.

Re: A Student's Guide to Preparing for Data Science Interviews

#33

Post by hellogoodbyeeee »

booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

I think the field is seeing a large amount of "title bloat". I've seen job postings for data scientist roles that are only looking for SQL and Microsoft Excel skills. I almost accepted a data scientist role at a small manufacturing company, but a more realistic job title would have been "Marketing Analyst"

Re: A Student's Guide to Preparing for Data Science Interviews

#34

Post by AlexCoventry »

minimaxir wrote: post #4

So I've been interviewing for data analyst/science positions since leaving Apple in April. I may do a postmortem on my search later, but speaking from my experience with many, many interviews over the past couple months, the TL;DR is that the conventional interview wisdom on Hacker News/the cscareerquestions subreddit/this article is wrong and out of date . Interviews for such positions require a different set of ski…

Hi, minimaxir. If confidentiality doesn't prohibit it, I'd be curious to know what the most interesting roles you interviewed for were.

Re: A Student's Guide to Preparing for Data Science Interviews

#35

Post by blahblah3 »

minimaxir wrote: post #7
alexchantavy wrote: post #6

Earlier quoted context omitted.

What kinds of technical questions do they ask in a data science interview?

On the stats side, often higher-level theory questions, such as "How does the k-means algorithm work?", "How do you select the best k for k-means?", "What is the curse of dimensionality?" which again would not be things covered at a data boot camp or data science thought pieces on Medium. On the technical side, there is often more-advanced SQL (nested JOINs + PostgreSQL window functions). On the big data side, there…

All the stats stuff you mentioned (and more) is laid out very nicely in the second chapter of "Elements of Statistical Learning" https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Re: A Student's Guide to Preparing for Data Science Interviews

#36

Post by mmierz »

reader5000 wrote: post #27
moab wrote: post #12

Earlier quoted context omitted.

I overheard some colleagues talking about a recent interview where a candidate with "stellar industry experience", i.e. Kaggle wins and previous ML experience at a valley company, who couldn't explain Bayes rule to them, let alone rederive Naive Bayes. While books like the one below are extremely theoretical, anyone interviewing for these kind of roles should spend at least a week or two just looking through this to…

This is like not hiring a [big name coding competition] winner because he didn't know radix sort.

I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C.

Trying to do data science with zero knowledge of the fundamentals of probability is dangerous. Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and it's absolutely necessary to understand the output of any machine learning model.

Re: A Student's Guide to Preparing for Data Science Interviews

#37

Post by mmierz »

booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

I think it's a dumb name for what I do so I call myself a programmer if anyone asks.

Re: A Student's Guide to Preparing for Data Science Interviews

#38

Post by huac »

bllguo wrote: post #2

Unfortunately there is very little here that isn't just general interview advice. As a new grad that went through the hunt very recently - it was a messy process. Very few places will consider you without extensive experience, or a masters/Ph.D. Of course if you're hiring people to research machine learning algorithms that's justifiable, but plenty of the responsibilities people associate with data scientists don't r…

I just graduated undergrad as well. The biggest factors assisting me in getting a DS position were:

1. Previous internship in data science 2. Experience developing R packages and putting them on GitHub 3. Really having statistical theory down pat

You don't need an advanced degree to be a data scientist but you need a strong understanding of stats and how to work with data. Having an advanced degree is a good indicator that you can do that. But it's not a prerequisite for an undergrad: Github, internships, TA-ships can make that up.

I think one advantage is that while PhD's are typically very good at the research process and the techniques used in their research, undergrads could be more flexible and adaptive to different situations.

Re: A Student's Guide to Preparing for Data Science Interviews

#39

Post by cat199 »

mindcrime wrote: post #31
softawre wrote: post #30

Earlier quoted context omitted.

Teaching me what I don't know is what I want with all of my engineering hires. I want people better than me who will tell me why my architecture isn't ideal or whatever the case is.

Yes, but I'd argue that's on a different level from "teach us why we need an 'architecture'" or "teach us how to use this 'data science' stuff". Some people are trying to be buzzword compliant when they don't actually understand the buzzwords. Ya know? Or maybe there's a better explanation for asking for a candidate with a Ph.D. in Statistics to create a linear regression model in Excel. Because truth is, for many co…

I get that it's difficult to get these gigs..

But also recall, that there are alot of people with general computer backgrounds and a whole lot more real-world work experience who could pick up the basics of data scicnce stuff as well..

Places where these jobs tend to be needed are often very highly placed and very strategic, with the decisions being made based on the data very critical to the overall company health. Some of what is being requested by requiring this level of credentials is real-world work experience and a certain level of maturity/professionalism that is often implied by this as well..

If the level of skill is basic enough that any other regular employee could learn it in short order, why wouldn't they and then have the cushy 'sit around and play with numbers all day' job too.. There needs to be enough 'there there' to placate internal politics as well..

Further, having a more advanced person would also likely need to be taking leadership roles in picking and rolling out solutions with long term costs associated with them, training other employees, etc, which also comes with the professionalism/experience part..

Re: A Student's Guide to Preparing for Data Science Interviews

#40

Post by achompas »

reader5000 wrote: post #24
booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

Technically anybody who uses Excel is a "data scientist". Just got to get the right buzzwords.

This is absurd and false. This person is an analyst of some sort.

Maybe this holds in the consulting world? It definitely does not hold in the tech world, IME.

Post reply on HN