Data Science Interview Study Guide
coriers.com
coriers.com
- I want to be excited about the possibility of becoming better at the topic, not fantasize about crushing an interview
- If there is a major difference between the skills needed for an interview and the actual job (as the concept of interview perparation suggests), that's bad in all kinds of ways
- The interview prep articles often present the material in an unattractive way, as a list of requirements that need to be fulfilled, without an explanation of how it all fits together.
How about leading the student along, from a small sample project, showing the pitfalls of not understanding statistics properly, to a larger project where the data is put into a database, motivating the study of SQL, to doing some programming related to the toy problem at hand, motivating a deeper dive into programming fundamentals? How about inspiring the student about the beauty of the topic, about the possibilities and powers they get with each new little skill that they acquire?
The problem is that the exact skills required for a job cannot be measured within the timespan of an interview, so instead closely related skills are measured. Until somebody solves this problem interview preparation makes a lot of sense.
Let's sit down at a specific problem I am working on. Let me tell you what I'm doing with the tools that I have. Your past work was in some way similar, and you can relate to what I'm doing in some way. Either you can jump right in and take over the keyboard, or you can tell me how you solved a similar problem with your tools and frameworks. I can show you how it's done with my setup, and teach you a few things in the process - which you will easily pick up as you have related experience. You got either questions that are exciting, maybe challenging me how I am doing it, or maybe teach me just a keyboard shortcut I didn't know, proving the time and effort you spent on this. We are both excited to learn a lot in a short amount of time and I know I want you to work with us...
It's far easier for the interviewer and the interviewee. Instead, it's all about covering yourself as the interviewer so you adopt the new garbage industry standard and pretend those are sound metrics. When the metrics are not that telling if a new hire is bad, you have an out that it's the process, not your mistake.
Here is what I think are some important skills in data science:
Can you understand the principles behind existing solutions and build a new solution from those principles?
(How fast) can you understand a new approach and apply it to a problem?
How long does it take you to translate a solution to working code? How optimal, readable and reusable is it?
Looking at your list of important skills in data science it seems we mostly agree on what is important...
This is another manifestation of the "Goodhart's Law"
I have failed twice the last year to be offered a DS position after going through all the interviews, references and HR personality tests. I would say that ~50% of the issue was me (0 not nailing every question) and the other 50% was the employer not understanding how to screen for the right people (= them lacking answers to questions they didn't ask).
I think there are two distinct skills: the skills you need for the job, and the skills you need to get the job. Maybe these guides cater to latter.
This kind of article can be valuable as efficient diagnostics way to prioritize skills worth learning.
E.g. I'm from a business background, but regularly use SQL and Python. I know I'd need those to work productively in data science. But do I also need to know Git? Best practices to avoid SQL injection? What a monad is? How to implement Djikstra's?
I'm interested to learn all of those things, but I also have a personal life and an entire second list of know-I-don't-knows in statistics. It can be helpful to get confirmation that "yeah, you know enough SQL, but it's worth learning a little bit about graph traversal."
Some data scientists take on more of a data engineering role, others data analysts and research scientist. It really depends what the company wants. The beginning of the post discusses various companies and what they might expect based on both personal experience and glass door.
Trust me, not many of us fantasize about crushing an interview. The purpose of the guide was to help people use their time efficiently to study for an interview so they could get on to the important stuff.
I think most tech people hate the interview process in general.
I think this interview process does a few things that companies like.
1. Creates a deterrent from leaving. Once you finally get that job...you don't want to have to go through that process again any time soon 2. It create exclusivity..like colleges...by creating a difficult process and reducing acceptance rates, when you do make it, it feels like you really earned it. 3. It's an easy way to fit people through a square hole like the GRE, SAT or ACT
Do we like it...no
Something I've noticed about data-science candidates is that they are very happy to jump into the technical details of an implemented model - but sometimes struggle on is communicating the reasons for the model in the first place and how it can help the company/research project. A lot of data-science projects are smaller ad-hoc jobs where the data scientist is trying to answer some business question and here communication is a vital skill.
Lol, well now, there’s a recipe for success right there.
I don't agree with the notion but that's where we are. It's one extreme or the other: extreme generalization with no depth or extreme depth with no generalization. Thats what pays and there's seemingly no reward for the in-between.
1. Metatron Discovery : https://metatron.app 2. Metabase : https://metabase.com/