I've been trying to learn some fundamentals of data science and machine learning recently when I ran into this medium article about Amazon interview questions. I think I can answer some of the ML and probability questions but others just fly off the top of my head. What do you all think ?

• How does a logistic regression model know what the coefficients are?

• Difference between convex and non-convex cost function; what does it mean when a cost function is non-convex?

• Is random weight assignment better than assigning same weights to the units in the hidden layer?

• Given a bar plot and imagine you are pouring water from the top, how to qualify how much water can be kept in the bar chart?

• What is Overfitting?

• How would the change of prime membership fee would affect the market?

• Why is gradient checking important?

• Describe Tree, SVM, Random forest and boosting. Talk about their advantage and disadvantages.

• How do you weight 9 marbles three times on a balance scale to select the heaviest one?

• Find the cumulative sum of top 10 most profitable products of the last 6 month for customers in Seattle.

• Describe the criterion for a particular model selection. Why is dimension reduction important?

• What are the assumptions for logistic and linear regression?

• If you can build a perfect (100% accuracy) classification model to predict some customer behaviour, what will be the problem in application?

• The probability that item an item at location A is 0.6 , and 0.8 at location B. What is the probability that item would be found on Amazon website?

• Given a ‘csv’ file with ID and Quantity columns, 50million records and size of data as 2 GBs, write a program in any language of your choice to aggregate the QUANTITY column.

• Implement circular queue using an array.

• When you have a time series data by monthly, it has large data records, how will you find out significant difference between this month and previous months values?

• Compare Lasso and Ridge Regression.

• What’s the difference between MLE and MAP inference?

• Given a function with inputs — an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers.

• When users are navigating through the Amazon website, they are performing several actions. What is the best way to model if their next action would be a purchase?

• Estimate the disease probability in one city given the probability is very low national wide. Randomly asked 1000 person in this city, with all negative response(NO disease). What is the probability of disease in this city?

• Describe SVM.

• How does K-means work? What kind of distance metric would you choose? What if different features have different dynamic range?

• What is boosting?

• How many topic modeling techniques do you know of?

• Formulate LSI and LDA techniques.

• What are generative and discriminative algorithms? What are their strengths and weaknesses? Which type of algorithms are usually used and why?”

I am currently a Lead Data Scientist at a large defense contractor, primarily applying data science solutions to business-facing homerooms. Think supply chain, business management, etc.

A few highlights about me...

• Very strong SQL skills, and I have done a large amount of data ETL

• Moderately strong Python skills

• Top 1% on Stack Overflow (I answer a lot of SQL and Python questions, also ask some)

• Nearly 10 internal Trade Secrets awarded to products I have built

• B.S. in Information Technology, I am graduating in August with my M.S. in Computer Science w/ an AI concentration from Hopkins

• About 3.5 years of work experience out of undergrad, two internships at Defense contractors before that

• Also have security related certifications (Security+)

• I mentor both the cybersecurity and AI clubs for my high school (along with a few other alumni)

I was contacted on LinkedIn by a recruiter. I have never really had an intention of working at FAANG organizations. From what I have read both on Reddit and elsewhere, the "work 7 days a week" and high pressure culture doesn't fit what I am really looking for. However, the recruiter mentioned almost 60% more than I make now, so that was enticing.

I feel technically sound -- but I definitely don't know how succinctly I could give an answer to some technical questions. I've looked at:

https://towardsdatascience.com/the-amazon-data-scientist-interview-93ba7195e4c9

https://towardsdatascience.com/amazon-data-scientist-interview-practice-problems-15b9b86e86c6

https://www.reddit.com/r/datascience/comments/dn5uxq/amazon_data_scienceml_interview_questions/

Are these good resources? Should I be prepared to write an algorithm from scratch? Would it be easier things, like kmeans, or am I expected to code backprop from scratch? I've done these things from scratch before, but I used reference material... I am nervous about not being able to demonstrate my skills because of being too focused on providing these overly technical answers.

Any advice is appreciated!

Edit: Wow! This blew up. I certainly was not expecting this much feedback, and certainly not so much kindness. As a somewhat new graduate ( < 5 years) who is still figuring out their own self confidence, getting to share a little bit of my background and my fears moving forward with you all has been cathartic, not to mention the sheer volume of incredibly useful feedback I have gotten. I am going to think some thing through tomorrow, and I'll be sure to update this post. If I go along with the interview, which I think i will based on this feedback, ill be sure to create an update post to let you all know what happened!

Last week, I got laid off because my employer decided to lay off approx 7% of the workforce which amounts to more than 1000 employees in the US.

Nonetheless, I have my first technical round for the Sr. Data Engineer interview with Amazon Pharmacy next week. I would be glad and grateful if people out here could help me in preparing for that. The first round is the live coding round (80% data structure and algorithms, 20 % SQL). Due to layoffs, I am feeling underconfident but this is probably my best chance to bounce back.

Thank you in advance!