Amazon data science interview questions

I had a similar experience there. There were parts of the company that seemed like they sucked, but my experience was only positive and put me in a position to learn a ton from strong engineers while building my skills up and making a good amount of money. When I started, I made less than some of my friends who chose startups, but two years out of college I was making more than almost anybody I knew.

I might not have worked on my passion in specific there, but I had a great time, learned a ton, and IMO set myself up for a great trajectory in my career.

Like most other large companies, YMMV depending on where you're at.

My quick and dirty answers are below. I'm thinking of moving jobs within the next year so I could use the practice. Can anyone do better?

> How do you treat colinearity?

Throw away the redundant part of the data

> How will you deal with unbalanced data where the ratio of negative and positive is huge?

This is very problem-dependent, but it's got the potential to wreak havoc with your learning algorithms. You might get seemingly good results by e.g. always predicting positive. Think carefully about your loss function.

> How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession and gender? Define a machine learning algorithm for this.

This is a messy combination of continuous, categorical, and binary data. I'd encode the data in a vector [salary, x-y co-ords, one-hot encoding of profession, and binary indicator for gender]. Something like a random forest will probably get you most of the way there. Unusual professions could mess with the algorithm, so consider grouping by industry or averaging with a model that omits the profession data.

> Is it useful to apply PCA to your data before SVM classification?

Probably, but it could be data dependent. Semi-supervised learning often improves machine learning models, and it'll lower the dimensionality of your inputs, which will make training / hyperparameter search far more efficient.

> How do you compare a neural network that has one layer, one input and output to a logistic regression model?

Not entirely sure I understand the question, but a NN is basically just nested logistic regression, depending on the activation function.

> From a long sorted list and a short 4 element sorted list, which algorithm will you use to search the long sorted list for 4 elements.

To be honest, I'd use binary search and call it a day. I realise this isn't the answer you're looking for.

> How will inspect missing data and when are they important for your analysis?

You might try to impute the missing data from similar datapoints. Maybe use something like K nearest neighbours on the non-missing components to deduce the missing values. Got to be careful doing this though, since it could massively bias your analysis: consider using a special encoding for "missing data" too.

> Estimate the probability of a disease in a particular city given that the probability of the disease on a national level is low.

This is a tough one, since there are so many ways to answer it. I think I'd start by writing down a list of factors that might allow a high prevalence of the disease locally despite a low prevalence nationally. Weather? Local wildlife? Proximity to major transport hubs? Then I might suggest a simple model like naive Bayes.

Then why do machine learning scientists get paid multiples more than statisticians? Why do CS departments get far more funding for ML grants than the same grant branded from the statistics department?

Say what you want about ML vs statistics, the fact is: in both academia and industry, machine learning is far sexier than statistics.

Yeah, my experience is almost a perfect match. These days I'm wondering how a statistician can best capitalize long-term in tech?

I will try.

> How do you treat colinearity?

Use an LASSO as a feature selector or regularizer.

> How will you deal with unbalanced data where the ratio of negative and positive is huge?

User F1 score instead of accuracy as a performance metric. Try using cascade classifier.

> How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession and gender? Define a machine learning algorithm for this.

More or less, the same as what you said.

> Is it useful to apply PCA to your data before SVM classification?

No, since kernel-SVM optimization is dependent on data (similarity) and not features. Also, we will be projecting the data into a basis expanded space anyways, so the dimensional reduction would be redundant.

> How do you compare a neural network that has one layer, one input and output to a logistic regression model?

An MLP-NN is the same as a logistic regressor, if all connections as 1-1 (no basis expansion), no activation function in the data-1st layer connection and Sigmoid as activation function in the 1st layer-output connection.

> From a long sorted list and a short 4 element sorted list, which algorithm will you use to search the long sorted list for 4 elements.

I am dreadfully bad at core CS questions...throw some AI questions at me. XD

> How will inspect missing data and when are they important for your analysis?

You might try to impute the missing data from similar datapoints. Maybe use something like K nearest neighbours on the non-missing components to deduce the missing values. Got to be careful doing this though, since it could massively bias your analysis: consider using a special encoding for "missing data" too.

Love your solutions. I might also try using cascade classifiers.

> Estimate the probability of a disease in a particular city given that the probability of the disease on a national level is low.

tough question. Would probably involve me asking a lot of questions about what data I have to play with. Very interview specific.