Category: Mock Interview

What Questions should Data Science candidates ask in Job Interviews?

28/10/2022

Interviews for data scientist, machine learning engineer, and AI-focused roles comprise several rounds during a typical on-site interview. These interviews assess candidates' prowess in technical (coding, statistics, machine learning, systems design), product (product metrics, product sense, business case), as well as leadership and behavioral skills.

In a typical hour long interview, candidates may get anywhere from 5 to 15 minutes to ask questions to the interviewers. However, most candidates do not prepare or think about questions to ask in advance. This is a big missed opportunity for candidates to learn more about the role, team, org, company, tech stack, culture, leadership values etc. directly from the current employees and future team mates.

In the context of data science, candidates ought to ask pertinent questions that may shed more light on the day-to-day work, projects, teams and the culture in the org. With greater interviewing and real-world data science experience, candidates will be able to better decipher the answers to such questions and read between the lines to make a more informed decision whether to join the company or not.

With everything else being more or less equal amongst the different job offers one may have, the quality of the hiring manager and team, organizational culture, learning and career growth prospects become decisive factors.

Following is a sample list of 20 questions to consider asking the hiring team, in no particular order:

What would be the first project I might work on?
What will I learn/do in the first 100 days on the job? First 6 months? First year?
How much time do I get to ramp up and onboard?
What is your data, modelling, and MLOps stack?
What models have you deployed to production so far?
What are the key organizational challenges in developing and deploying AI projects?
What are the key AI/ML use cases that you own?
How are AI/ML use cases identified, developed, and prioritised?
What has been the business impact of the AI/ML team/org?
What is the typical timeline to take models from conception to production?
Do you have separate data and machine learning engineers vs data scientists?
Do you have labeled data for the various use cases? If not, how do you label data?
Which stakeholder teams do you closely collaborate with? How does it work?
What is the leadership's view of the importance of AI for the growth of the business?
Does the team focus more on short-term deliverables or long-term projects?
Do you believe in open-sourcing and publishing the AI output from your team?
How does my career path look like as I grow with the company?
How much scope do I have to propose and develop my own ideas for AI projects?
How do you define success for a data scientist as an individual and as a team?
How will you ensure that I succeed in my role?

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What Questions should Data Science candidates ask in Job Interviews?

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