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OneAssist Consumer Solutions Data Scientist Interview Questions and Answers

Updated 12 May 2021

OneAssist Consumer Solutions Data Scientist Interview Experiences

1 interview found

I applied via Campus Placement and was interviewed in Apr 2021. There were 6 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Asked everything related to Machine learning , Deep Learning , NLP , Stats , Tableau

Interview Preparation Tips

Interview preparation tips for other job seekers - They are expecting a entry role with 3-5 years skillset, had my 1st technical for 2 hrs and second technical for 2 hrs , eventhough didn't get selected .

Interview questions from similar companies

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Nov 2023. There were 2 interview rounds.

Round 1 - Coding Test 

Simple staright forward necessary domain questions

Round 2 - HR 

(5 Questions)

  • Q1. What are joins you have worked onshowcase. Them
  • Q2. What is query folding power query
  • Ans. 

    Query folding in Power Query is the process of pushing data transformation steps back to the data source for more efficient query execution.

    • Query folding helps optimize query performance by minimizing data transfer between the data source and Power Query.

    • It allows Power Query to generate and execute SQL queries against the data source instead of loading all data into memory for processing.

    • Query folding is particularly ...

  • Answered by AI
  • Q3. What are earlier projects worked on
  • Q4. How to you handle load effectively
  • Ans. 

    I handle load effectively by prioritizing tasks, delegating when necessary, and utilizing time management techniques.

    • Prioritize tasks based on deadlines and importance

    • Delegate tasks to team members to distribute workload

    • Utilize time management techniques such as Pomodoro technique or Eisenhower matrix

    • Stay organized with to-do lists and calendars

    • Communicate with team members to ensure everyone is on the same page

  • Answered by AI
  • Q5. Walk through the challenges you faced
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-

I applied via LinkedIn and was interviewed in Feb 2024. There were 2 interview rounds.

Round 1 - Coding Test 

Focus on little bit of dynamic programming

Round 2 - Technical 

(5 Questions)

  • Q1. Apache spark architecture and ecosystem along with memory management
  • Q2. SQL queries for manager salary
  • Q3. Python decorators --> @
  • Q4. RANK and other window functions
  • Q5. Joins along with use case
  • Ans. 

    Joins are used in SQL to combine rows from two or more tables based on a related column between them.

    • Joins are used to retrieve data from multiple tables based on a related column

    • Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

    • Use cases for joins include combining customer data with order data, merging employee information with department details

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

30 min - basic Python coding

Round 2 - One-on-one 

(2 Questions)

  • Q1. DataEngineering Basic Questins
  • Q2. Personality based questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Practice basic concepts

I applied via Naukri.com and was interviewed in Jun 2022. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - One-on-one 

(12 Questions)

  • Q1. What is correlation(in plain english)?
  • Ans. 

    Correlation is a statistical measure that shows how two variables are related to each other.

    • Correlation measures the strength and direction of the relationship between two variables.

    • It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.

    • Correlation does not imply causation, meaning that just because two variables are correlat...

  • Answered by AI
  • Q2. What is multi-collinearity?
  • Ans. 

    Multicollinearity is a phenomenon where two or more independent variables in a regression model are highly correlated.

    • It can lead to unstable and unreliable estimates of regression coefficients.

    • It can also make it difficult to determine the individual effect of each independent variable on the dependent variable.

    • It can be detected using correlation matrices or variance inflation factors (VIF).

    • Solutions include removing...

  • Answered by AI
  • Q3. What are p-values? explain it in plain english without bringing up machine learning?
  • Ans. 

    P-values are a statistical measure that helps determine the likelihood of obtaining a result by chance.

    • P-values range from 0 to 1, with a smaller value indicating stronger evidence against the null hypothesis.

    • A p-value of 0.05 or less is typically considered statistically significant.

    • P-values are commonly used in hypothesis testing to determine if a result is statistically significant or not.

  • Answered by AI
  • Q4. How are LSTMs better than RNNs? what makes them better? how does LSTMs do better what they do better than vanilla RNNs?
  • Ans. 

    LSTMs are better than RNNs due to their ability to handle long-term dependencies.

    • LSTMs have a memory cell that can store information for long periods of time.

    • They have gates that control the flow of information into and out of the cell.

    • This allows them to selectively remember or forget information.

    • Vanilla RNNs suffer from the vanishing gradient problem, which limits their ability to handle long-term dependencies.

    • LSTMs ...

  • Answered by AI
  • Q5. Does pooling in CNNs have any learning?
  • Ans. 

    Pooling in CNNs has learning but reduces spatial resolution.

    • Pooling helps in reducing overfitting by summarizing the features learned in a region.

    • Max pooling retains the strongest feature in a region while average pooling takes the average.

    • Pooling reduces the spatial resolution of the feature maps.

    • Pooling can also help in translation invariance.

    • However, too much pooling can lead to loss of important information.

  • Answered by AI
  • Q6. Why does optimisers matter? what's their purpose? what do they do in addition to weights-updation that the vanilla gradient and back-prop does?
  • Ans. 

    Optimizers are used to improve the efficiency and accuracy of the training process in machine learning models.

    • Optimizers help in finding the optimal set of weights for a given model by minimizing the loss function.

    • They use various techniques like momentum, learning rate decay, and adaptive learning rates to speed up the training process.

    • Optimizers also prevent the model from getting stuck in local minima and help in ge...

  • Answered by AI
  • Q7. What does KNN do during training?
  • Ans. 

    KNN during training stores all the data points and their corresponding labels to use for prediction.

    • KNN algorithm stores all the training data points and their corresponding labels.

    • It calculates the distance between the new data point and all the stored data points.

    • It selects the k-nearest neighbors based on the calculated distance.

    • It assigns the label of the majority of the k-nearest neighbors to the new data point.

  • Answered by AI
  • Q8. You have two different vectors with only small change in one of the dimensions. but, the predictions/output from the model is drastically different for each vector. can you explain why this can be the case...
  • Ans. 

    Small change in one dimension causing drastic difference in model output. Explanation and solution.

    • This is known as sensitivity to input

    • It can be caused by non-linearities in the model or overfitting

    • Regularization techniques can be used to reduce sensitivity

    • Cross-validation can help identify overfitting

    • Ensemble methods can help reduce sensitivity

    • It is generally a bad thing as it indicates instability in the model

  • Answered by AI
  • Q9. Slope vs gradient (again not in relation to machine learning, and in plain english)
  • Ans. 

    Slope and gradient are both measures of the steepness of a line, but slope is a ratio while gradient is a vector.

    • Slope is the ratio of the change in y to the change in x on a line.

    • Gradient is the rate of change of a function with respect to its variables.

    • Slope is a scalar value, while gradient is a vector.

    • Slope is used to describe the steepness of a line, while gradient is used to describe the direction and magnitude o...

  • Answered by AI
  • Q10. How are boosting and bagging algorithms different?
  • Ans. 

    Boosting and bagging are ensemble learning techniques used to improve model performance.

    • Bagging involves training multiple models on different subsets of the data and averaging their predictions.

    • Boosting involves training multiple models sequentially, with each model focusing on the errors of the previous model.

    • Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.

    • Examples of bagging al...

  • Answered by AI
  • Q11. What is a logarithm? (in linear algebra) what is it's significance and what purpose does it serve?
  • Ans. 

    A logarithm is a mathematical function that measures the relationship between two quantities.

    • Logarithms are used to simplify complex calculations involving large numbers.

    • They are used in linear algebra to transform multiplicative relationships into additive ones.

    • Logarithms are also used in data analysis to transform skewed data into a more normal distribution.

    • Common logarithms use base 10, while natural logarithms use

  • Answered by AI
  • Q12. What are gradients? (not in relation to machine learning)
  • Ans. 

    Gradients are the changes in values of a function with respect to its variables.

    • Gradients are used in calculus to measure the rate of change of a function.

    • They are represented as vectors and indicate the direction of steepest ascent.

    • Gradients are used in optimization problems to find the minimum or maximum value of a function.

    • They are also used in physics to calculate the force acting on a particle.

    • Gradients can be cal

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - be strong in fundamentals and be able to explain what and why of every project on your resume and all things things used in those projects.

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Time Series forecasting questions

Interview Preparation Tips

Interview preparation tips for other job seekers - They are happy with me send an intent to offer, but later declined as requirements changed (time waste)

I applied via LinkedIn and was interviewed in Sep 2022. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Home Assignment 

(5 Questions)

  • Q1. What is shebang in bash scripting
  • Ans. 

    Shebang is a special character sequence used in Unix-based systems to indicate the interpreter for a script file.

    • Shebang starts with #! and is followed by the path to the interpreter

    • It is used to specify the interpreter for a script file

    • It allows the script to be executed without explicitly invoking the interpreter

    • Example: #!/bin/bash specifies that the script should be run using the bash interpreter

  • Answered by AI
  • Q2. Explain vector projection
  • Ans. 

    Vector projection is the process of projecting a vector onto another vector.

    • It involves finding the component of one vector that lies along another vector.

    • The result is a scalar value that represents the length of the projection.

    • The projection can be calculated using the dot product of the two vectors.

    • The formula for vector projection is proj_u(v) = (u . v / ||u||^2) * u.

    • Vector projection is used in various fields such

  • Answered by AI
  • Q3. Who is a leader and qualities of leader.
  • Ans. 

    A leader is someone who guides and inspires others towards a common goal.

    • A leader possesses strong communication skills and can effectively convey their vision and goals to their team.

    • A leader is able to motivate and inspire their team members, encouraging them to perform at their best.

    • A leader is knowledgeable and experienced in their field, earning the respect and trust of their team.

    • A leader is able to make tough de...

  • Answered by AI
  • Q4. A week to spend on answering a question on Probability of collision of two objects.
  • Q5. How do you convert 3d object collision problem to 2d collision problem
  • Ans. 

    3D object collision problem can be converted to 2D by projecting the objects onto a 2D plane.

    • Project the 3D objects onto a 2D plane using a projection matrix.

    • Calculate the 2D coordinates of the projected objects.

    • Perform collision detection in 2D using standard algorithms.

    • Example: projecting a sphere onto a 2D plane results in a circle.

    • Example: projecting a cube onto a 2D plane results in a square.

  • Answered by AI

Interview Preparation Tips

Topics to prepare for Digantara Data Scientist interview:
  • Orbital Mechanics
Interview preparation tips for other job seekers - Watch out folks, this company ghosts candidates, each interview takes place after almost an entire month!

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
-
Process Duration
2-4 weeks
Result
Not Selected

I was interviewed in Sep 2023.

Round 1 - HR 

(2 Questions)

  • Q1. Why are you interested in smartsheet
  • Q2. Tell me about your ds background
Round 2 - Technical 

(1 Question)

  • Q1. Case interview question.
Interview experience
5
Excellent
Difficulty level
Hard
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. What is your Experience in Deploying ML Models
  • Ans. 

    I have experience deploying ML models in production environments using cloud services like AWS and Azure.

    • Deployed ML models using AWS SageMaker for real-time predictions

    • Utilized Azure Machine Learning service to deploy models for batch processing

    • Implemented CI/CD pipelines for automated model deployment

    • Managed model versioning and monitoring in production environments

  • Answered by AI
  • Q2. What is your Expertise in deep learning methodologies
  • Ans. 

    I have expertise in deep learning methodologies including neural networks, CNNs, RNNs, and GANs.

    • Extensive experience with neural networks and their applications in image recognition and natural language processing

    • Proficient in Convolutional Neural Networks (CNNs) for tasks such as image classification and object detection

    • Familiarity with Recurrent Neural Networks (RNNs) for sequential data analysis like time series for...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Referral and was interviewed before Jun 2023. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. Model evaluation methods to check model performance
  • Ans. 

    Various model evaluation methods are used to check the performance of a data science model.

    • Cross-validation: splitting data into multiple subsets for training and testing

    • Confusion matrix: evaluating classification models based on true positives, true negatives, false positives, and false negatives

    • ROC curve: plotting the true positive rate against the false positive rate

    • Precision, recall, and F1 score: metrics for evalu...

  • Answered by AI
  • Q2. Projects done so far with tech explanation
  • Ans. 

    I have worked on projects involving machine learning algorithms for predictive analytics and natural language processing.

    • Developed a predictive model using random forest algorithm to forecast customer churn in a telecom company.

    • Implemented sentiment analysis using natural language processing techniques to analyze customer reviews for a retail company.

    • Utilized deep learning techniques such as LSTM for time series foreca

  • Answered by AI

Skills evaluated in this interview

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