By Purvanshi Mehta – Towards Data Science
Preparing for your interviews is one aspect of the job search and getting a job that you want is another aspect of it. I wrote about the first part recently which makes this post more important. I have seen so many people struggling to make decisions about which job to take. Since most companies cannot talk about the actual work they are doing beforehand, it’s difficult to get to know what exactly does the team work on and what will your life look like after joining the company.
I know the question: “So do you have any questions for me now” after an hour-long interview can be daunting. But it is important to know how your life will look after you end your job search with an offer in hand!
I have curated a list of questions that I wish I had read before I was making a decision of choosing between multiple job offers. Most questions are generic but have some specific questions for Data scientists, ML engineers, applied scientists, data engineers, and research engineer roles. Please let me know of more questions and I will keep adding here (you can email me on email@example.com or put in the comments)
1. What tech stack do you use in your everyday?
This is an important question as most companies have their own internal technologies for working on things on production. Will you be working on such internal technologies or something common like python/spark or whatever you like. Working on internal technologies doesn’t actually create transferable skills and it may require more effort on your part to be up to date with the industry standards.
2. What does your average day/week look like?
What time will you be most investing on? There can be several follow-up questions that can be asked in this section
- How many hours on average do you spend on meetings? This may obviously vary with different roles. Like if you are in a managerial role you might spend almost all your time on meetings but if your role is more of an engineer this tells about how many people will you be interacting with on a daily basis, how much time is spent on just communications, how much time will you be spending doing actual work. The third part will determine
- What does your work-life balance look like?
3. Research-related questions
How to determine how research-oriented your job will be? This was an important point for me. These are the questions that helped me analyze better
- How much time do you spend reading research papers? Is there a reading group you attend weekly/biweekly?
- Will you be able to remain on top of the ML craziness even after your day-to-day job or will you just only remember BERT from 2018 (Yeah it’s been 3 years already!)
- Does your team publish? If so in what conferences?
- Is publishing a priority in your team or just working on production models? Is publishing considered a factor in promotions? What type of conferences are the current publications? Are these very applied or there are some fundamental research questions that have been answered?
- Does your team prefer publishing in internal conferences/journals? Internal conferences help in getting acquainted with the research in other groups of the company, networking, and especially collaborations.
4. Open source projects
- Does the team have an open-source project? This helps in maintaining an external portfolio and gives an idea about the type of work your team is involved in.
- Can you contribute to other open-source projects? There are many companies that restrict you from working on other open-source projects outside of your work. You can check this with your manager if this is something you are interested in.
5. What are the core values of the company? Do your values align with these?
This tells a lot about how the company will treat its employees and basically you. Also how much will they value you!
6. What diversity efforts does the team make?
People can have contrasting opinions about this but this has always been an important choice for me when deciding career options.
7. How long does a project last?
Will you be working on dozens of small projects or will you be given ownership of one part of the product?
8. What is the ratio of software engineering vs data analysis vs machine learning work?
This is such an important question. So many people are frustrated with just data cleaning or extracting jobs rather than actual problem-solving. See if this ratio fits you.
Another follow-up question that could be asked is:
9. Do you have a separate research/software engineering/data engineering team?
10. What does the procedure to propose a new project in the group, look like?
Does the group have a specific meeting monthly/biweekly just for knowledge/idea sharing? This makes everyone heard and also shows that the team values innovation.
11. What does growth look like on your team?
Does this align with your personal career goals? Another important point will be to see how many people transition to other roles. For example- if you like the role of product management will that jump be possible from your current role?
12. How old is the product part you will be working on?
In Debarghya Das’s interview with Jay Shah, he mentioned how most teams are now using other people’s code and most of the difficult problems have already been solved. I couldn’t agree more with this. If you are working on a product that has been there for years now, then the most difficult part of product build has already been solved. It is mostly building on top of that or maintaining the current codebase. This comes down to a lot of incremental work.