Jumping out of Amazon Data Science

Hi blinders,
I've finally managed to escape after about a year. Just wanted to post my experience of Amazon (Supply chain/logistics) data science.

First off, if you really want to join Amazon as a Data Scientist, apply for an Applied Scientist position. In Amazon, a DS is expected to have the science knowledge of an AS but no programming skills. Only SQL. Put simply, Amazon DS == world Data Analyst++. Amazon AS == world DS. You do actual data science as an Applied Scientist.

Because the DS requirements are so weird, you basically get any random project that the bosses need to get done. Which, given the horrible data infra, means spending about 99% of your time writing and editing SQL...and then converting them to excel, because supply chain bosses are dinosaurs who think big data = big excel sheets.

The few DS that I saw doing good work on the science side were mostly asked to replicate another researcher's work. Probably the most demotivating part is that you will be 1 of 20 teams trying to solve the same science problem (using SQL) because Amazon is that big and it can afford to do that.

The requirements from PMs go along the lines of "build me a prediction model with unlabeled data, that also tells me why it predicted it, what part of the business is causing problems and also auto fixes it. Also, document all the fixes so that we get promoted.". And that request will come from a person whose title would be Head of ML/Data/Research. If it predicts wrong, then model performance == your performance rating.

Most DS managers come through non-science backgrounds (because "no programming skills required"). Many are BIEs who convinced their L7s to change their titles to DS to be more marketable to the outside world. Good for them, but you get stuck with DS managers who have never built any models, let alone production level models on large datasets. What you get is a manager who asks you to go from zero to production in 2 weeks on Amazon sized data, forcing you to build very basic models that runs quuckly (think OLS...random forests ≠ Frugality on AWS resources) on millions of data points. You never build upon that knowledge because there isn't any time (unless you aim to get PIPed).

Needless to say, this was my observation in the supply chain side. Life could possibly be greener in other orgs. I left the company because the culture was dull. Most of the work was busy work, maintainence overhead, 100s of meaningless document reviews (->this puts gov bureaucracy to shame on time wastage), non-existent engineering culture/norms, etc.

As a DS, a big company like Amazon does not need you. It has fulls orgs to deal with any of the more interesting problems. Only way to get into these orgs is by being an AS. Side note: As an AS, there is a slightly higher probability of doing some good work. But it doesn't guarantee that you end up doing SQL. I had a friend who got hired as a Research Scientist. His "research" was to dig into 3000 lined SQL codes to identify bugs.

I finally got out (covid slowed my departure). Actually happy to not be doing sql and building shitty recommender systems instead (at least I get to build them ;)). My personal recommendation for new DS would be to join a small company whee you can actually learn to build even basic things. I bet you that you will not improve your knowledge at amazon.

New TC: 140k
YOE: 3.5yrs

#datascience #data