A day in the life of a Product Data Scientist working from anywhere at Spotify 

Nastinka, thanks for the genuine video, I like how you present the way you go about your day and what kind (work) mentality you have. I recently finished my Bsc in clinical nutrition & dietetics, but moved into junior DevOps while trying to figure our which data path would fit me most. Your job looked most fun, spasiba :) Plus points for 'colleagues socializing' + 1.000 point for cute cat encounter Also thank for showing your surrounding it look for a moment to me like: ohh Mosku is like West Europe and then moments later.. ah no, it's like most soviet countries XD

With time we see that everything is fine, and the pressure descreases. Take care!!!

Is this a troll?

I have watched your videos. They are great too. All the best friend!

Hi Would love if you could do a video of this on your channel. This is specifically the role I am also interested in and I would love to hear what INSIGHTS you might have. I am also interested in working remotely for a foreign company, so would also be great if you can share some tips on that. I would

Eyyy qué onda, damn espero puedas leer esto, me gustaría hacer mi cambio de carrera a Data science y tengo algunas duda sobre el proceso, si te pudiera contactar de alguna manera me ayudarías mucho.😩🙏🏻

thanks 😊

Love from india 🇮🇳🇮🇳

There are many options, it doesn’t have to be a data science degree. Good if it covers math, statistics, and software development in various degrees.

You don't have to, but if you study software development or data science, you could.

Notion and Google Cal

Wow! You sound like a very driven child. Wish you the best of luck. :) 1. Career opportunities: In my previous job, I mentored a youngster who had just passed 10th grade. He joined our team as an intern before joining 11th and worked with us on some simple NLP tasks. He was able to leverage his knowledge on probability and Matrices to be able to grasp some intuition around some NLP concepts. 2. Track: I'm sure @Anastasia will do a much better job at laying down the track, but until she is able to make a video on it, i would suggest the following: 2.1. Descriptive Statistics: Your school curriculum will lay a strong groundwork on statistics on rectangular data. As you strengthen your fundamentals, start using Excel to get a taste of how most data scientists start their journey. You can download something called as a "Dataset" from the internet and just start understanding basic commands on Excel to perform some operations. 2.2. As you get closer to 10th grade, you can start learning more about probability which acts as the basis for many solutions that are proposed in the data science world. As you come towards finishing 10th grade, try to see if you can get some intuition around "distributions", and try to understand a little about popular ones like Normal, Bimodal, etc.

Just one note: This free advice is only given to help drive the energy of an ambitious and driven child in the right direction for their dreams. This doesn't mean that you can find success in the field only if you start this early or that you should start getting into the rat race this early. ☺️ Please enjoy your time as a student and grasp as much knowledge as possible from all disciplines. They will act as a great source of inspiration when you decide to apply the tools from the data science field onto problems that inspire you the most. 😊

Jupyter Notebooks or JupyterLab are widely used. As well as RStudio, Sublime, Atom, XCode, and pretty much any other IDEs including some developed internally in the companies people work in. It's up to ones comfort and preferences often.

Hard to say how many hours, sometimes a whole day, sometimes less than 30 minutes a day. Predominantly SQL and Python, BigQuery and JupyterLab.

Yup, I decided to split out the previous video out of this one. It was way too long otherwise.

@@AnastasiaKVL i love this video

it is very common, you do not need a degree there are an abundance of resources right here on youtube to get you started.

As i understand, most people with the designation of ML Engineer work on designing systems and productionalising machine learning models. Different companies/teams have different responsibilities for various designations, but your work would be focusing on implementing software engineering practices that general purpose programmers deal with, and also working specifically to solve problems that come in the way of bringing a model to production. If you have enough understanding of some ML models, your first job will teach you a lot in getting an understanding of the entire pipeline for most systems. Unless your team is specifically dealing with NLP tasks, I think you can first focus on implementing solutions for areas you are already familiar with, without getting paralysed.

Good luck with your journey ☺️

yes! Sweden is my home now :)

This of course depends on your CV, experience, and dedication to the application process, but I don't see why not if you put effort into it :)

@@AnastasiaKVL thank you... I would surely follow it ... Love your content ... And amazing vlogs

notion 😊

notion

@@AnastasiaKVL thank you so much

Experience and great, relevant project work is more important than grades :)

@@AnastasiaKVL Thanks dear mam

из Москвы