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Sigmoid Senior Data Scientist Lead Interview Questions and Answers
Sigmoid Senior Data Scientist Lead Interview Experiences
1 interview found
Anonymous
Selected
I applied via Naukri.com and was interviewed in Jun 2024. There were 5 interview rounds.
(4 Questions)
- Q1. How does dropout help in neural networks?
- Ans.
Dropout helps prevent overfitting in neural networks by randomly setting a fraction of input units to zero during training.
Dropout helps in preventing overfitting by reducing the interdependence between neurons
It acts as a regularization technique by randomly setting a fraction of input units to zero during training
Dropout forces the network to learn redundant representations, making it more robust and generalizable
It ...
- Q2. How does xgboost deal with nan values?
- Ans.
XGBoost can handle missing values (NaN) by assigning them to a default direction during tree construction.
XGBoost treats NaN values as missing values and learns the best direction to go at each node to handle them
During tree construction, XGBoost assigns NaN values to the default direction based on the training data statistics
XGBoost can handle missing values in both input features and target variables
- Q3. What would you do if you see your model not performing well on a time series prediction specifically on peaks and troughs?
- Q4. How would you deal with datasets having lots of categories
- Ans.
Utilize feature engineering techniques like one-hot encoding or target encoding to handle datasets with many categories.
Use feature engineering techniques like one-hot encoding to convert categorical variables into numerical values
Consider using target encoding to encode categorical variables based on the target variable
Apply dimensionality reduction techniques like PCA or LDA to reduce the number of features
Use tree-b...
Case study involved creating a churn model with an imbalanced dataset. It contained a lot of missing values in numerical features which were correlated, Also the scaling was highly skewed. Categorical data contained a lot of low frequency categories. They wanted a final model performance on a test dataset on chosen KPIs (I chose F1-score).
(2 Questions)
- Q1. Questions on the case study - assumptions made, why did you choose a particular KPI? What was the loss function? How did you deal with class imbalance, nan values , high number of categories? Did you perfo...
- Q2. Questions related to past experience - I told him about my last project which was based on computer vision. Interviewer asked a lot of clarifying questions and inquired about the process
(4 Questions)
- Q1. Asked about previous projects and the business impact.
- Q2. What challenges have you faced managing a team?
- Q3. Asked about a hypothetical scenario, how would you help the customer with that
- Q4. What would you do if someone in your team is not performing well
(2 Questions)
- Q1. Salary Discussion
- Q2. Previous employment history
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
Kushal Kulkarni
I was interviewed in May 2024.
Questions based on ML,PYTHON, DATA VISUALIZATION
(2 Questions)
- Q1. What is TF-IDF IN NLP
- Ans.
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
TF-IDF stands for Term Frequency-Inverse Document Frequency
It is used in Natural Language Processing (NLP) to determine the importance of a word in a document
TF-IDF is calculated by multiplying the term frequency (TF) by the inverse document frequency (IDF)
It helps in identifying the most important
- Q2. Python coding questions based on list
Interview Preparation Tips
ML,DL,Python,NLP,Data VIsualization
(1 Question)
- Q1. Explain TF-IDF in NLP
- Ans.
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
TF-IDF stands for Term Frequency-Inverse Document Frequency.
It is used in Natural Language Processing (NLP) to determine the importance of a word in a document.
TF-IDF is calculated by multiplying the term frequency (TF) of a word by the inverse document frequency (IDF) of the word.
It helps in ident...
Selected
I applied via Naukri.com and was interviewed before Dec 2023. There were 3 interview rounds.
Test of Basic data structures in Python include lists, tuples, and dictionaries, as well as loops and conditional statements.
Framework and requirements for chatbot implementation.
(1 Question)
- Q1. Salary discussion
Sayantan Ghosh
Interview Questionnaire
1 Question
- Q1. What friends think of you?
- Ans.
My friends think of me as reliable, supportive, and always up for a good time.
Reliable - always there when they need help or support
Supportive - willing to listen and offer advice
Fun-loving - enjoys socializing and trying new things
Interview Preparation Tips
Experience: After Resume Shortlist we had an aptitute round.
Tips: Answer according to your own judgement. Dont try to be too precise.
Round: HR Interview
Experience: I said they think I am a workaholic as I prefer to complete my work before chilling with them.
College Name: NIT Durgapur
Interview Preparation Tips
Experience: Interview at 11 pm. Stressed environment, close to stress interview.
SELECTION PROCEDURE:
1.Online Test
2. GD
3. PI(HR)
GD TOPICS :
Topic 1 : How can education system benefit from interdisciplinary methods.
Topic 2 : Interconnected problems in the field of movie making.
INTERVIEW EXPERIENCE:
So you can speak German? Describe MS Dhoni in german. They opened Google Translate to counter check the words they wanted to be translated in both Deutsch and Spanish. Your profile speaks of an inclination towards software skills, why do you want to join an analytics company? Justify your action in two reasons as to why are you sitting here interviewing for the post of a data scientist rather than apply for a software engineer when this CV speaks highly of computer science? What is Finite Element Method? Explain. How relevant is your work in Computer Vision? Breakdown the tagline of Audi and translate accordingly. What is "Technik für Mobel" ? What are your current projects? Answer : Microsoft Xbox Kinect, Gesture Recognition. Counter question : But at Musimga you'd be doing far simpler stuff.? Counter suggestion : Why don't you go for MS?
Tips: Keep your cool during counter questions. Prepare your profile and CV well. Rest all is your hard work and groomed personal talents and acquired skills you learnt over the internet.
Skills: Ability To Cope Up With Stress, Spanish, German, Finite Element Modeling - FEM, Foreign Language
College Name: NIT Raipur
Funny Moments: Another HR enters in the midst of my interview and asks with bewildered amazement : What language is he speaking?
The other HR, "German".
rustam garg
I applied via Recruitment Consultant and was interviewed in Dec 2018. There were 3 interview rounds.
Interview Questionnaire
11 Questions
- Q1. 1. Why Machine Learning?
- Q2. 2. Why did you choose Data Science Field?
- Ans.
I chose Data Science field because of its potential to solve complex problems and make a positive impact on society.
Fascination with data and its potential to drive insights
Desire to solve complex problems and make a positive impact on society
Opportunity to work with cutting-edge technology and tools
Ability to work in a variety of industries and domains
Examples: Predictive maintenance in manufacturing, fraud detection
- Q3. 3. What about Linear Regression? (Theory Part)
- Q4. 4. What is the difference between Linear Regression and Logistic Regression?
- Ans.
Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.
Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.
Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.
Linear Regr...
- Q5. 5. Explain Confusion Matrix?
- Ans.
Confusion matrix is a table used to evaluate the performance of a classification model.
It is a 2x2 matrix that shows the number of true positives, false positives, true negatives, and false negatives.
It helps in calculating various metrics like accuracy, precision, recall, and F1 score.
It is useful in identifying the strengths and weaknesses of a model and improving its performance.
Example: In a binary classification p...
- Q6. 6. Can we use confusion matrix in Linear Regression?
- Ans.
No, confusion matrix is not used in Linear Regression.
Confusion matrix is used to evaluate classification models.
Linear Regression is a regression model, not a classification model.
Evaluation metrics for Linear Regression include R-squared, Mean Squared Error, etc.
- Q7. 7. Explain KNN Algorithm?
- Ans.
KNN is a non-parametric algorithm used for classification and regression tasks.
KNN stands for K-Nearest Neighbors.
It works by finding the K closest data points to a given test point.
The class or value of the test point is then determined by the majority class or average value of the K neighbors.
KNN can be used for both classification and regression tasks.
It is a simple and easy-to-understand algorithm, but can be compu
- Q8. 8. Explain Random Forest and Decision Tree?
- Ans.
Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy.
Random Forest is a type of supervised learning algorithm used for classification and regression tasks.
It creates multiple decision trees and combines their outputs to make a final prediction.
Each decision tree is built using a random subset of features and data points to reduce overfitting.
Ran...
- Q9. 9. One Tricky Mathematical Question !
- Q10. 10. What are the Projects you have done?
- Ans.
I have worked on various projects involving data analysis, machine learning, and predictive modeling.
Developed a predictive model to forecast customer churn for a telecommunications company.
Built a recommendation system using collaborative filtering for an e-commerce platform.
Performed sentiment analysis on social media data to understand customer opinions and preferences.
Implemented a fraud detection system using anom...
- Q11. I didn't get shortlisted for 2nd Round.
Interview Preparation Tips
They will ask you about the projects you have mentioned in resume and all the questions will be from that part.
Skills: Communication, Body Language, Problem Solving, Analytical Skills
Duration: 1-4 weeks
Skills evaluated in this interview
General aptitude basics
Mcq and basic ml model building
Interview Preparation Tips
I applied via Approached by Company
(3 Questions)
- Q1. Explain Transformers how different from previous RNN, LSTM etc.
- Ans.
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.
Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.
Transformers have been shown ...
- Q2. What are different types of Attention?
- Ans.
Different types of Attention include self-attention, global attention, and local attention.
Self-attention focuses on relationships within the input sequence itself.
Global attention considers the entire input sequence when making predictions.
Local attention only attends to a subset of the input sequence at a time.
Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
- Q3. Difference between GPT and BERT model
- Ans.
GPT is a generative model while BERT is a transformer model for natural language processing.
GPT is a generative model that predicts the next word in a sentence based on previous words.
BERT is a transformer model that considers the context of a word by looking at the entire sentence.
GPT is unidirectional, while BERT is bidirectional.
GPT is better for text generation tasks, while BERT is better for understanding the cont
(1 Question)
- Q1. Difference between Data scientist, ML and AI
- Ans.
Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
Data scientists analyze large amounts of data to uncover patterns and insights.
Machine learning involves developing algorithms that improve automatically through experience.
Artificial intelligence r...
Skills evaluated in this interview
Gajjala Deepthi
Selected
I applied via Naukri.com and was interviewed in Jun 2024. There were 4 interview rounds.
First round is coding round where two use cases are there. Need to solve them
(1 Question)
- Q1. They will all topics Statistics, SQL, Python, Machine Learning, Data Science
(1 Question)
- Q1. They will discuss more on the projects what we worked on
(1 Question)
- Q1. Salary Discussion
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