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Obviously.Ai Data Scientist Interview Questions and Answers
Obviously.Ai Data Scientist Interview Experiences
4 interviews found
Anonymous
(2 Questions)
- Q1. Time series and classical ml
- Q2. Machine learning pipeline
(2 Questions)
- Q1. Asked about my goals and ambitions
- Q2. Did some quick checks to know my background
Difficult but not bad was able to ace it
Conversation with a senior data scientist and the leadership
I was interviewed in Apr 2024.
(3 Questions)
- Q1. Python Data frame manipulation
- Q2. SQL join questions
- Q3. Api call function
Create time series forecasting models from data preparation, feature engineering to model training and selection
Selected
I applied via AngelList and was interviewed in Sep 2023. There were 3 interview rounds.
It was Machine Learning assignment checking knowledge of classical ML.
(1 Question)
- Q1. CV grilling and General ML DS Question
Interview questions from similar companies
(3 Questions)
- Q1. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
- Q2. Parameters of Decision Tree
- Ans.
Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
Max depth: maximum depth of the tree
Min samples split: minimum number of samples required to split an internal node
Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')
Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
- Q3. Explain any one of your project in detail
- Ans.
Developed a predictive model to forecast customer churn in a telecom company
Collected and cleaned customer data including usage patterns and demographics
Used machine learning algorithms such as logistic regression and random forest to build the model
Evaluated model performance using metrics like accuracy, precision, and recall
Provided actionable insights to the company to reduce customer churn rate
Skills evaluated in this interview
I was interviewed in Oct 2024.
(1 Question)
- Q1. Project related questions from your CV
(2 Questions)
- Q1. Question on transformers
- Q2. Comparison of transfer learning and fintuning.
- Ans.
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
Transfer learning uses knowledge gained from one task to improve learning on a different task.
Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.
Transfer learning is faster and requires less data compared to training a...
Skills evaluated in this interview
I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.
(2 Questions)
- Q1. SQL and pandas coding
- Q2. Resume projects deep dive
Interview Preparation Tips
Selected
I applied via Approached by Company and was interviewed in Aug 2024. There were 2 interview rounds.
*****, arjumpudi satyanarayana
(5 Questions)
- Q1. What is the python language
- Ans.
Python is a high-level programming language known for its simplicity and readability.
Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.
It emphasizes code readability and uses indentation for block delimiters.
Python has a large standard library and a vibrant community of developers.
Example: print('Hello, World!')
Example: import pandas as pd
- Q2. What is the code problems
- Ans.
Code problems refer to issues or errors in the code that need to be identified and fixed.
Code problems can include syntax errors, logical errors, or performance issues.
Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.
Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
- Q3. What is the python code
- Ans.
Python code is a programming language used for data analysis, machine learning, and scientific computing.
Python code is written in a text editor or an integrated development environment (IDE)
Python code is executed using a Python interpreter
Python code can be used for data manipulation, visualization, and modeling
- Q4. What is the project
- Ans.
The project is a machine learning model to predict customer churn for a telecommunications company.
Developing predictive models using machine learning algorithms
Analyzing customer data to identify patterns and trends
Evaluating model performance and making recommendations for reducing customer churn
- Q5. What is the lnderssip
- Ans.
The question seems to be incomplete or misspelled.
It is possible that the interviewer made a mistake while asking the question.
Ask for clarification or context to provide a relevant answer.
Interview Preparation Tips
- Python
- Machine Learning
Skills evaluated in this interview
I applied via Naukri.com and was interviewed in Sep 2024. There were 2 interview rounds.
(3 Questions)
- Q1. Overfitting and Underfitting
- Q2. Find Nth-largest element
- Ans.
Find Nth-largest element in an array
Sort the array in descending order
Return the element at index N-1
- Q3. NLP Data preprocessing
(2 Questions)
- Q1. Salary Discussion
- Q2. Fitment discussion
Skills evaluated in this interview
I applied via Naukri.com and was interviewed in Jul 2024. There were 2 interview rounds.
(3 Questions)
- Q1. Tell me about yourself?
- Ans.
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
Background in statistics and machine learning
Experience in solving complex problems using data-driven approaches
Passionate about leveraging data to drive insights and decision-making
- Q2. Describe in detail about one of my main project.
- Ans.
Developed a predictive model for customer churn in a telecom company.
Collected and cleaned customer data including usage patterns and demographics.
Used machine learning algorithms such as logistic regression and random forest to build the model.
Evaluated model performance using metrics like accuracy, precision, and recall.
Implemented the model into the company's CRM system for real-time predictions.
- Q3. Few questions related to my projects.
(1 Question)
- Q1. Questions on Basics python(Since i am fresher)
Interview Preparation Tips
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