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Paynearby Technologies Data Scientist Interview Questions and Answers
Paynearby Technologies Data Scientist Interview Experiences
1 interview found
Anonymous
Selected
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
Case Study - Churn Prediction
(4 Questions)
- Q1. ML Questions, Python Questions, SQL Questions
- Q2. Explain logistic regression
- Ans.
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
Logistic regression is used when the dependent variable is binary (e.g., 0/1, yes/no).
It estimates the probability that a given input belongs to a certain category.
The output of logistic regression is a probability score between 0 and 1.
It uses the logistic function (sigmoid function) ...
- Q3. Different types of validation
- Ans.
Different types of validation include cross-validation, holdout validation, and leave-one-out validation.
Cross-validation involves splitting the data into multiple subsets and training the model on different combinations of these subsets.
Holdout validation involves splitting the data into a training set and a validation set, and training the model on the training set before evaluating it on the validation set.
Leave-one...
- Q4. Outlier, overfitting, underfitting
(1 Question)
- Q1. Hiring Manager Round
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
Selected
I applied via Referral and was interviewed before Feb 2023. There was 1 interview round.
(2 Questions)
- Q1. What is clustering
- Ans.
Clustering is a technique used in data analysis to group similar data points together based on their characteristics.
Clustering is an unsupervised learning method.
It helps in identifying patterns and relationships in data.
Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.
Example: Grouping customers based on their purchasing behavior.
Example: Identifying different species of flowers based
- Q2. How to avoid overfitting
- Ans.
To avoid overfitting, use techniques like cross-validation, regularization, and increasing training data.
Use cross-validation to evaluate model performance on unseen data
Apply regularization techniques like L1 or L2 regularization to penalize complex models
Increase the size of the training dataset to provide more diverse examples
Use feature selection or dimensionality reduction methods to reduce the complexity of the m...
Interview Preparation Tips
Skills evaluated in this interview
Sql and pandas problems along mcqs on ML
(2 Questions)
- Q1. Linear Regression
- Q2. Basic Neural Network Implementation
(2 Questions)
- Q1. Question about working of BERT
- Q2. Word2vec explanation
- Ans.
Word2vec is a technique used to create word embeddings by training a neural network on a large corpus of text.
Word2vec is a shallow neural network model that learns to represent words as vectors in a continuous vector space.
It captures semantic relationships between words by placing similar words close together in the vector space.
There are two main architectures for Word2vec: Continuous Bag of Words (CBOW) and Skip-gr...
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed in Jul 2024. There were 2 interview rounds.
(1 Question)
- Q1. Question regarding my resume .It is technical interview
Case study-this is about loan default prediction assignment
First Question is basic coding question (Term frequecy of words).
Then a case study.
Basic ML questions.
Interview Questionnaire
1 Question
- Q1. Basic interview questions
Interview Preparation Tips
I applied via Referral and was interviewed in Oct 2024. There were 3 interview rounds.
Sql, python, statistical questions
(2 Questions)
- Q1. How daily work looks like?
- Ans.
Daily work involves analyzing data, creating reports, and communicating findings to stakeholders.
Reviewing and cleaning data sets
Creating visualizations and dashboards
Conducting statistical analysis
Communicating insights to non-technical stakeholders
- Q2. Most ise tools?
- Ans.
Some of the most commonly used tools for data analysis include Excel, Python, R, Tableau, and SQL.
Excel is widely used for data manipulation and analysis.
Python and R are popular programming languages for data analysis and machine learning.
Tableau is a powerful data visualization tool.
SQL is essential for querying databases and extracting data.
4 sql tasks on 3 tables. Joins, subqueries
Interview Preparation Tips
Sql mandatory
Skills evaluated in this interview
I applied via Approached by Company and was interviewed in Apr 2024. There were 2 interview rounds.
(1 Question)
- Q1. What techonologies do you know?
- Ans.
I am proficient in SQL, Python, R, Tableau, and Excel for data analysis and visualization.
SQL
Python
R
Tableau
Excel
SQL Coding Tests. You get 3 tables and they ask you to find customer with certain conditions. Not hard
I applied via LinkedIn and was interviewed before Nov 2023. There were 2 interview rounds.
(1 Question)
- Q1. Brief me about yourself
(1 Question)
- Q1. What is your experience as a data analyst?
(2 Questions)
- Q1. Case Study of zomato ?
- Q2. Some basic math questions. Simultaneous equations .
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Paynearby Technologies Data Scientist Interview Process
based on 1 interview
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