Data Warehouse ➔ Lead Data Scientist Job Interview Questions and Answers

Lead Data Scientist interview preparation guide for freshers and experienced professionals. Explore these number of Lead Data Scientist frequently asked questions(FAQs) extracted from many job interviews

☆ Please explain star schema?
☆ Do you know what are Recommender Systems?
☆ Tell us what are Recommender Systems?
☆ Explain me what are your technical competencies?
☆ Please explain what is Collaborative Filtering?
☆ Tell us what are Eigenvalue and Eigenvector?
☆ Do you know what is selection Bias?
☆ Tell me do gradient descent methods at all times converge to a similar point?
☆ Can you please explain survivorship bias?
☆ Do you know what is the Law of Large Numbers?
☆ Do you know what is root cause analysis?
☆ Do you know what is pruning in Decision Tree?
☆ Tell me what challenges have you encountered while working with big data?
☆ What are the different kernels functions in SVM?
☆ Tell me how regularly must an algorithm be updated?
☆ Tell me how do you identify a barrier to performance?
☆ Explain me what is the goal of A/B Testing?
☆ Tell me what is logistic regression?
☆ Tell me what is the difference between Regression and classification ML techniques?
☆ Do you know how many data structures does R language have?
☆ Tell me what is the difference between supervised and unsupervised machine learning?
☆ Explain me a big data project you worked on?
☆ Please explain what does not ‘R’ language do?
☆ Do you know what are the types of biases that can occur during sampling?
☆ Tell me what is selection bias?
☆ Tell me how can you produce co-relations and covariances?
☆ Tell me why do you want to work at this company as a data scientist?
☆ Tell me which data object in R is used to store and process categorical data?
☆ Tell me how has your prior experience prepared you for a role in data science?
☆ Tell us how do you work towards a random forest?
☆ Tell me what are the drawbacks of the linear model?
☆ Explain me what is TF/IDF vectorization?
☆ Tell me how to create a function in arguments using apply() in R?
☆ Tell me how do you overcome challenges to your findings?
☆ Explain me what is the difference between machine learning and deep learning?
☆ What are the steps in making a decision tree?
☆ Can you explain me what is logistic regression? Or State an example when you have used logistic regression recently?
☆ Tell me what is exploding gradients?
☆ Do you know more functions in brief in R?
☆ Tell me how do clean up and organize big data sets?
☆ Explain me nchar() in R string manipulation?
☆ Can you describe strsplit() in R string manipulation?
☆ Tell me what methods do you use to identify outliers within a data set?
☆ Tell us why is resampling done?
☆ Tell me what cross-validation technique would you use on a time series dataset?
☆ Tell me you develop a big data model, but your end user has difficulty understanding how the model works and the insights it can reveal. How do you communicate with the user to get your points across?
☆ Explain me for loop control statement in R?
☆ Do you know what is the difference between rnorm and runif functions?
☆ Tell us how would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
☆ Tell us what tools or devices help you succeed in your role as a data scientist?
☆ Can you please explain selective bias?
☆ Tell us what are confounding variables?
☆ Please explain me cross-validation?
☆ Explain me what are feature vectors?
☆ Do you know what regularization is and why it is useful?
☆ Tell me what is Random Forest? How does it work?
☆ Tell me what is bias, variance trade off?
☆ Tell me how do you define big data?
☆ Do you know how to Merge the files into a single dataframe?
☆ Can you write the syntax to set the path for a current working directory in R environment?

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