Data Warehouse :: Lead Data Scientist Job Interview Questions and Answers

Lead Data Scientist Frequently Asked Questions in various Lead Data Scientist job interviews by interviewer. The set of questions are here to ensures that you offer a perfect answer posed to you. So get preparation for your new job interview

1 Can you write the syntax to set the path for a current working directory in R environment?
2 Do you know how to Merge the files into a single dataframe?
3 Tell me how do you define big data?
4 Tell me what is bias, variance trade off?
5 Tell me what is Random Forest? How does it work?
6 Do you know what regularization is and why it is useful?
7 Explain me what are feature vectors?
8 Please explain me cross-validation?
9 Tell us what are confounding variables?
10 Can you please explain selective bias?
11 Tell us what tools or devices help you succeed in your role as a data scientist?
12 Tell us how would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
13 Do you know what is the difference between rnorm and runif functions?
14 Explain me for loop control statement in R?
15 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?
16 Tell me what cross-validation technique would you use on a time series dataset?
17 Tell us why is resampling done?
18 Tell me what methods do you use to identify outliers within a data set?
19 Can you describe strsplit() in R string manipulation?
20 Explain me nchar() in R string manipulation?
21 Tell me how do clean up and organize big data sets?
22 Do you know more functions in brief in R?
23 Tell me what is exploding gradients?
24 Can you explain me what is logistic regression? Or State an example when you have used logistic regression recently?
25 What are the steps in making a decision tree?
26 Explain me what is the difference between machine learning and deep learning?
27 Tell me how do you overcome challenges to your findings?
28 Tell me how to create a function in arguments using apply() in R?
29 Explain me what is TF/IDF vectorization?
30 Tell me what are the drawbacks of the linear model?
31 Tell us how do you work towards a random forest?
32 Tell me how has your prior experience prepared you for a role in data science?
33 Tell me which data object in R is used to store and process categorical data?
34 Tell me why do you want to work at this company as a data scientist?
35 Tell me how can you produce co-relations and covariances?
36 Tell me what is selection bias?
37 Do you know what are the types of biases that can occur during sampling?
38 Please explain what does not ‘R’ language do?
39 Explain me a big data project you worked on?
40 Tell me what is the difference between supervised and unsupervised machine learning?
41 Do you know how many data structures does R language have?
42 Tell me what is the difference between Regression and classification ML techniques?
43 Tell me what is logistic regression?
44 Explain me what is the goal of A/B Testing?
45 Tell me how do you identify a barrier to performance?
46 Tell me how regularly must an algorithm be updated?
47 What are the different kernels functions in SVM?
48 Tell me what challenges have you encountered while working with big data?
49 Do you know what is pruning in Decision Tree?
50 Do you know what is root cause analysis?
51 Do you know what is the Law of Large Numbers?
52 Can you please explain survivorship bias?
53 Tell me do gradient descent methods at all times converge to a similar point?
54 Do you know what is selection Bias?
55 Tell us what are Eigenvalue and Eigenvector?
56 Please explain what is Collaborative Filtering?
57 Explain me what are your technical competencies?
58 Tell us what are Recommender Systems?
59 Do you know what are Recommender Systems?
60 Please explain star schema?

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