# Why do you want to switch? # Where do you see yourself in 5 yrs? # What is data science from your point of view? # How do you approach a data analysis debug problem , give specific examples # How big was your data set ? How big is the data set to be considered is large data set? # Explain a DOE that you designed and ran # What lithography development project you worked on explain # What happens if dose and focus changes. # What happens if focus is increases or decreased, what happens if the resist is changed from positive to negative? # If there issue for printing in litho step, can etch fix it and vice versa # What is annular/quadrapole , what do they improve in litho printing? # What is Depth of focus? # How off axis printing is helpful? # How is grating features helpful in litho printing? # How to do you ensure that a vulnerable features are printed properly?
Senior Data Scientist Interview Questions
3,392 senior data scientist interview questions shared by candidates
They asked me what did I solve/fix with a project outcome. And they presented a Python code and asked me to find 8 mistakes in the code.
Descrivimi qual'è un tuo difetto
How would you build a model the estimates the probability of closing a deal and presents it as score.
Behavioural: tell me about a time you made a mistake at work Coding: simple tasks on live coding on data manipulation System design: how would you build a data product that did X Recap the challenge: why this model and present your results
Typical questions regarding resume and experience.
Have you built a CLV system before?
All about past projects and my tech stack
1. Brief Self-Introduction in All the Rounds. 2. Recruiter: What do you know about the business? Why are you interested in the role within the business? Projects/Qualifications/Skills overview relevant to the role. 3. Hiring Manager: Projects deep dive questions relevant to the role. 4. Team: Data Structures, OOPS, Ethics and Governance, Measuring Sucess, Goals, Code structure, and Key components, Python-type questions.
Round 2: Many questions and cross questions on your approach to the case study - why would you choose this data, what else could you do. Other what-if scenarios on this. In modelling, fundamental questions of how some model works - not superficial level answers but demanded thorough answers.
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