Coffee doesn't fuel only code: articles by developers for developers
Have you ever dreamt going in a supermarket and finding all your favorite products ready for you on a single shelf?
Are you the kind of person who’d like to get the grocery done as fast as possible, and move onto more interesting stuffs?
Or would you like finding all the things you usually buy ready for you, and spend more time exploring offers and new products?
Continue reading “Everli Cart Assistant”
One of the questions I get more frequently when I say that I am a Data Engineer is “but what do you do?”. Depending on who you ask, you can receive a variety of answers, especially related to the tools and skills we may use. If you ask me that question and you know about data processes, I will answer “we’re the backend of the Data Scientists”. If you don’t have a clue about data-related jobs, I will answer “we organize big amounts of data”. And by “organize”, what I mean is “clean, model, calculate, store and make available”.
In Everli, I’m in charge of data related to our shoppers, from the moment they apply to one of our job offers until they deliver an order to a customer. This process involves more factors than you may think because we need to have the right amount of shoppers recruited and the right amount of shoppers working exactly when they are needed. The main goal is to balance a good service to our customers and the wellbeing of our shoppers.
This balance has been a challenge since the beginning, and this example is only one more step in finding the best fit for our needs. It has also been a data modeling challenge, and a great opportunity to dig deeper into shopper data. And now I can explain one of the things a Data Engineer does!
Continue reading “Saturation, or how much time did our couriers actually work?”Here we go again.
This is the second episode of our docuseries about how we are managing retailers’ data for our beloved customers.
If you did not read our previous post about this topic, you should spend a few minutes catching up on it.
With this part, we would like to introduce the hidden aspect of our product and I promise, it is not a buzzword: Machine Learning.
In the following paragraphs, we are going to show new technical improvements and the analytics aspects under the hood.