3 ms·

Where are these jobs where you can interview this badly and still get hired because in my experience DS interviews are extremely hard and often expect people to

Where are these jobs where you can interview this badly and still get hired because in my experience DS interviews are extremely hard and often expect people to have very high Stats skills as well as Data Structures/Algo skills at FAANG level.

semi-extrinsic 4y ago
These days if you have a company selling cat food or rivets for aerospace or providing taxi swrvice to a random city, or whatever, they might have a few data scientists helping them make "optimized" business choices. Obviously they won't have a very adcanced recruiting process for that.
- quickthrower2 4y ago
  Like the market for lemons: https://en.m.wikipedia.org/wiki/The_Market_for_Lemons https://en.m.wikipedia.org/wiki/The_Market_for_Lemons
noelsusman 4y ago
It's different at a lot of non-tech companies. I'm in the nonprofit world and my interview barely had any technical component at all.
- bo1024 4y ago
  To be able to tell whether a candidate is good, the hiring team has to be expert! No chicken, no egg.
bootsmann 4y ago
I think the issue here is that "data science" encompasses two very distinct branches of work. One answers to business needs and the other produces data based solutions for the product itself i.e you might have a data scientist who A/B tests your website design so you minimize your churn rate and the other is the team at uber eats who maintains the recommendation engine. While the distinction might not always be as sharp, the former makes up the bulk of data scientists in the market (and I suspect the OP is in that boat) with comparably simple interviews while the rest is the 5 step interview process with hackerrank test you are more familiar with.
- CapmCrackaWaka 4y ago
  Yes we definitely fall into more traditional "predictive modeling" data science than deep learning / recommendation algo roles.
  - wirrbel 4y ago
    I think the distinction is not so much on the domain/application. Rather it’s just that many Organisations decided to jump on the data-science wagon and don’t quite know yet for what qualities to look out for during hiring. And in second order as long as the predictive model is not included in a business process the over fitting is not as easily visible to the layperson stakeholders (and junior data scientists).
random314 4y ago
The ML interviews at FAANG are absurdly simple. Design YouTube recommendations for which canned answers are readily available. A simple stats question. If I double the number of samples, how much will the confidence interval change? Most FAANG ML engineers can't answer this question.
- adamsmith143 4y ago
  And then reverse a binary tree?
  - random314 4y ago
    What's your point? The question about sample sizes is arcane trivia?
    - adamsmith143 4y ago
      For someone on an ML team? Yes. You could spend years building computer vision models and not once think about sample size.
      - random314 4y ago
        The Dunning-Kruger effect is strong here. "What I know is what makes me the expert. What I don't know is irrelevant". The definition of Standard deviation is in chapter 1 of Stats 101. https://www.google.com/search?q=standard+deviation&tbm=isch https://www.google.com/search?q=standard+deviation&tbm=isch Apparently, asking Stats 101 chapter 1 question of a so called "Data Scientist" is too much of an irrelevant question! > expect people to have very high Stats skills Or as you have made apparent, expect people to have ZERO stats skills! Some of the innumerate activities I have observed in "expert" data scientists and ML engineers who have years of experience without once thinking about sample sizes 1. Using A/B tests to accept the Null hypothesis instead of rejecting it 2. Squandering away 30M $ in annual revenue because they wanted to avoid a situation/meeting in which they might look like they don't understand statistics. This is hilarious because they simply nodded their head as if they understand all the calculations and then simply dropped any other meetings or followups and left 30M $ on the table 3. Not refreshing a key revenue generating model for 18 months because the were "trying to figure out" why the AUC was improving when the performance on "golden set data" was dropping 4. Using thresholding and aggregation to produce poor quality distorted training data of rich perfectly sampled data 5. Trying to use A/B tests to estimate impact even when the control and variant are not independent All of the above at FAANGS! My coworkers in a non FAANG company were much more sophisticated. These are the kind of candidates a "build recommendations for youtube" interview selects. Template appliers. The list of stupidities goes on and on! But yeah, none of them think that a basic understanding of statistics is necessary for work. The good thing about Javascript engineers is that they don't have an understanding of Statistics and are aware of it. However the DS/MLEs are unskilled and unaware of it.

semi-extrinsic 4y ago

These days if you have a company selling cat food or rivets for aerospace or providing taxi swrvice to a random city, or whatever, they might have a few data scientists helping them make "optimized" business choices. Obviously they won't have a very adcanced recruiting process for that.

quickthrower2 4y ago
Like the market for lemons: https://en.m.wikipedia.org/wiki/The_Market_for_Lemons https://en.m.wikipedia.org/wiki/The_Market_for_Lemons

noelsusman 4y ago

It's different at a lot of non-tech companies. I'm in the nonprofit world and my interview barely had any technical component at all.

bo1024 4y ago
To be able to tell whether a candidate is good, the hiring team has to be expert! No chicken, no egg.

bootsmann 4y ago

I think the issue here is that "data science" encompasses two very distinct branches of work. One answers to business needs and the other produces data based solutions for the product itself i.e you might have a data scientist who A/B tests your website design so you minimize your churn rate and the other is the team at uber eats who maintains the recommendation engine. While the distinction might not always be as sharp, the former makes up the bulk of data scientists in the market (and I suspect the OP is in that boat) with comparably simple interviews while the rest is the 5 step interview process with hackerrank test you are more familiar with.

CapmCrackaWaka 4y ago
Yes we definitely fall into more traditional "predictive modeling" data science than deep learning / recommendation algo roles.
- wirrbel 4y ago
  I think the distinction is not so much on the domain/application. Rather it’s just that many Organisations decided to jump on the data-science wagon and don’t quite know yet for what qualities to look out for during hiring. And in second order as long as the predictive model is not included in a business process the over fitting is not as easily visible to the layperson stakeholders (and junior data scientists).

random314 4y ago

The ML interviews at FAANG are absurdly simple. Design YouTube recommendations for which canned answers are readily available. A simple stats question. If I double the number of samples, how much will the confidence interval change? Most FAANG ML engineers can't answer this question.

adamsmith143 4y ago
And then reverse a binary tree?
- random314 4y ago
  What's your point? The question about sample sizes is arcane trivia?
  - adamsmith143 4y ago
    For someone on an ML team? Yes. You could spend years building computer vision models and not once think about sample size.
    - random314 4y ago
      The Dunning-Kruger effect is strong here. "What I know is what makes me the expert. What I don't know is irrelevant". The definition of Standard deviation is in chapter 1 of Stats 101. https://www.google.com/search?q=standard+deviation&tbm=isch https://www.google.com/search?q=standard+deviation&tbm=isch Apparently, asking Stats 101 chapter 1 question of a so called "Data Scientist" is too much of an irrelevant question! > expect people to have very high Stats skills Or as you have made apparent, expect people to have ZERO stats skills! Some of the innumerate activities I have observed in "expert" data scientists and ML engineers who have years of experience without once thinking about sample sizes 1. Using A/B tests to accept the Null hypothesis instead of rejecting it 2. Squandering away 30M $ in annual revenue because they wanted to avoid a situation/meeting in which they might look like they don't understand statistics. This is hilarious because they simply nodded their head as if they understand all the calculations and then simply dropped any other meetings or followups and left 30M $ on the table 3. Not refreshing a key revenue generating model for 18 months because the were "trying to figure out" why the AUC was improving when the performance on "golden set data" was dropping 4. Using thresholding and aggregation to produce poor quality distorted training data of rich perfectly sampled data 5. Trying to use A/B tests to estimate impact even when the control and variant are not independent All of the above at FAANGS! My coworkers in a non FAANG company were much more sophisticated. These are the kind of candidates a "build recommendations for youtube" interview selects. Template appliers. The list of stupidities goes on and on! But yeah, none of them think that a basic understanding of statistics is necessary for work. The good thing about Javascript engineers is that they don't have an understanding of Statistics and are aware of it. However the DS/MLEs are unskilled and unaware of it.