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Fynd Data Scientist Interview Questions, Process, and Tips
Top Fynd Data Scientist Interview Questions and Answers
- Q1. Why cross entropy loss is used in classification, why not SSE?
- Q2. Explain LSTM and how to use for forecasting
- Q3. Best gpu operations for building a huge ml model
Fynd Data Scientist Interview Experiences
2 interviews found
Anonymous
(1 Question)
- Q1. Best gpu operations for building a huge ml model
- Ans.
Utilize GPUs for matrix multiplication, deep learning operations, and parallel processing.
Use GPUs for matrix multiplication to speed up computation.
Utilize GPUs for deep learning operations like training neural networks.
Take advantage of GPUs for parallel processing to handle large datasets efficiently.
Skills evaluated in this interview
Selected
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
(2 Questions)
- Q1. Why cross entropy loss is used in classification, why not SSE?
- Ans.
Cross entropy loss is used in classification because it penalizes incorrect classifications more heavily, making it more suitable for classification tasks compared to SSE.
Cross entropy loss is more suitable for classification tasks because it penalizes incorrect classifications more heavily than SSE.
Cross entropy loss is commonly used in scenarios where the output is a probability distribution, such as in multi-class c...
- Q2. How does RNN work?
- Ans.
RNN is a type of neural network that can process sequential data by retaining memory of previous inputs.
RNN stands for Recurrent Neural Network.
It has loops in the network, allowing information to persist.
RNNs are commonly used in natural language processing and time series analysis.
Example: Predicting the next word in a sentence based on previous words.
(2 Questions)
- Q1. Explain encoder decoder
- Ans.
Encoder-decoder is a neural network architecture used for tasks like machine translation and image captioning.
Encoder processes input data and generates a fixed-length representation
Decoder takes the representation and generates output data
Commonly used in tasks like machine translation (e.g. translating English to French) and image captioning
- Q2. Explain LSTM and how to use for forecasting
- Ans.
LSTM (Long Short-Term Memory) is a type of recurrent neural network that is capable of learning long-term dependencies.
LSTM is designed to overcome the vanishing gradient problem in traditional RNNs.
It has three gates: input gate, forget gate, and output gate, which control the flow of information.
LSTM is commonly used for time series forecasting, such as predicting stock prices or weather patterns.
To use LSTM for fore...
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
Bhupender S
I applied via LinkedIn and was interviewed in Jun 2024. There were 3 interview rounds.
(2 Questions)
- Q1. Probability related
- Q2. Stats related like IQR etc.
(2 Questions)
- Q1. Difference between entropy & information gain
- Ans.
Entropy measures randomness in data, while information gain measures the reduction in uncertainty after splitting data.
Entropy is used in decision trees to measure impurity in a dataset before splitting it.
Information gain is used in decision trees to measure the effectiveness of a split in reducing uncertainty.
Entropy ranges from 0 (pure dataset) to 1 (completely impure dataset).
Information gain is calculated as the d...
- Q2. LSTM & GRU, Which to use when ?
- Ans.
LSTM for longer sequences, GRU for faster training and less complex models.
Use LSTM for tasks requiring long-term dependencies and memory retention.
Use GRU for faster training and simpler models with fewer parameters.
Consider using LSTM for tasks like language translation or speech recognition.
Consider using GRU for tasks like sentiment analysis or text generation.
Time Series data were given, we have to provide some insights
Skills evaluated in this interview
It contain both Aptitude and Coding about base models and Deep learning too
(2 Questions)
- Q1. What different models technique ?
- Ans.
Different models techniques include linear regression, decision trees, random forests, support vector machines, and neural networks.
Linear regression is used for predicting continuous values.
Decision trees are used for classification and regression tasks.
Random forests are an ensemble method based on decision trees.
Support vector machines are used for classification tasks.
Neural networks are used for complex pattern re
- Q2. What are performance metric where to use what?
- Ans.
Different performance metrics are used for different types of machine learning models to evaluate their effectiveness.
For classification models, metrics like accuracy, precision, recall, F1 score, and ROC-AUC are commonly used.
For regression models, metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared are commonly used.
For clustering models, metrics like silhouette score and Davies-Bouldin in...
(2 Questions)
- Q1. Explain about Project
- Q2. What are problems faced in that project?
Skills evaluated in this interview
Abhilasha Dimble
I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.
(1 Question)
- Q1. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...
Interview Preparation Tips
Starts interview with lots of questions..
He interrupts me in every question's answer.. doesn't even ready listen my answers ...
Selected
I applied via Campus Placement and was interviewed in Nov 2023. There was 1 interview round.
(1 Question)
- Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
Asif Shaik
I applied via Indeed and was interviewed in Aug 2022. There were 4 interview rounds.
(5 Questions)
- Q1. Tell me about yourself?
- Q2. Projects you have done?
- Q3. Technical Skills You have?
- Q4. They asked about Any Certification course i have done?
- Q5. Could you tell me about our company?
Assignment is about IBM COGNOS!
They want to Know about my behaviour and How can I solve critical Conditions of company!
Interview Preparation Tips
- Know about Company
Selected
I applied via campus placement at The LNM Institute of information Technology, Jaipur and was interviewed before Jan 2024. There were 3 interview rounds.
The session lasted three hours and covered a wide range of topics, including DBMS, operating systems, SQL, computer networks, command-line interface commands, and data science topics such as Python, Pandas, and NumPy. Advanced topics included error handling and various algorithms like classification and clustering, along with deep learning concepts. Additionally, two data structures and algorithms questions were addressed, one being moderate and the other challenging, focusing on dynamic programming with bitmasking.
(4 Questions)
- Q1. What are the basics of data science?
- Q2. What are the best practices for handling large data sets ?
- Q3. What is the purpose of a confusion matrix in data science?
- Q4. Discussion around Kubernetes ?
(2 Questions)
- Q1. Can you provide an overview of yourself and your past experiences?
- Q2. How effective do you find it when interviewers check your confidence by asking apparent questions that may lead to hesitation in your responses?
Interview Preparation Tips
(1 Question)
- Q1. Basic questions on resume back propagation , gradient descent , activation functions and what is the significance why resnet , vanishing gradient poroblem
Data Scientist Interview Questions & Answers
Intellect Design Arena
Krishna Sai
posted on 22 Aug 2024
Selected
I applied via LinkedIn and was interviewed before Aug 2023. There were 4 interview rounds.
(2 Questions)
- Q1. About the Projects I was working on and step by step detailing
- Q2. Since it is NLP related projects mostly on Embeddings and word related questions
Its a take-home assignment related to NLP multi-class classification
(2 Questions)
- Q1. Again they discussed my insights on the projects
- Q2. Some case studies and how we can solve it
(2 Questions)
- Q1. Salary Negotiation
- Q2. Accepting offer
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Fynd Data Scientist Interview Process
based on 2 interviews
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