GE Aviation Senior Data Scientist Interview Questions and Answers

Q1. Node js working in detail
Ans. 
Node.js is a runtime environment that allows you to run JavaScript on the server side.
• Node.js is built on Chrome's V8 JavaScript engine.

• It uses an event-driven, non-blocking I/O model that makes it lightweight and efficient.

• Node.js is commonly used for building server-side applications, APIs, and real-time applications.

• It has a large ecosystem of libraries and frameworks, such as Express.js and Socket.io.
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Q2. Timer working in node
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In Node.js, timers can be implemented using functions like setTimeout() and setInterval().
• Use setTimeout() to execute a function once after a specified delay

• Use setInterval() to execute a function repeatedly at a specified interval

• Clear a timer using clearTimeout() or clearInterval()

• Example: setTimeout(() => { console.log('Timer executed!'); }, 2000);
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Q1. HLD questions on db and backend
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Q2. Relational non relational db
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Technical and coding

Q1. Technical questions
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Q1. Salary negotiations
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Data analysis , SQL, python, ML

Excellent performance

Q1. Tell me about your self
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Q2. Are you comfortable to position
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Q1. Power bi project experience, basics of sql, technical documentation
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Q1. Discussion related to Salary and Date of joining
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The aptitude test is pretty basic and logical

Q1. Based on the skills and case study you have mentioned in the resume and brainstorming ques too.
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Q1. Basic discussion. Simplest round.
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Q1. SQL question was asked
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Q2. Business acumen
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Based upon the profile vary, time series related problems, power BI

Q1. Deep profile discussion
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Q2. Futuristic opinion analysis
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Q1. Salary discussion
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Q2. Basic questions
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Q1. What are the types of ML algorithms? Give an example of each.
Ans. 
There are several types of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
• Supervised learning: algorithms learn from labeled data to make predictions or classifications (e.g., linear regression, decision trees)

• Unsupervised learning: algorithms find patterns or relationships in unlabeled data (e.g., clustering, dimensionality reduction)

• Reinforcement learning: algorithms l...read more
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Q2. How do you do time series classification?
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Time series classification involves using machine learning algorithms to classify time series data based on patterns and trends.
• Preprocess the time series data by removing noise and outliers

• Extract features from the time series data using techniques such as Fourier transforms or wavelet transforms

• Train a machine learning algorithm such as a decision tree or neural network on the extracted features

• Evaluate the performan...read more
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Q3. What is PCA and where and how it is used?
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PCA stands for Principal Component Analysis. It is a statistical technique used for dimensionality reduction.
• PCA is used to reduce the number of variables in a dataset while retaining the maximum amount of information.

• It is commonly used in data preprocessing and exploratory data analysis.

• PCA is also used in image processing, speech recognition, and finance.

• It works by transforming the original variables into a new set...read more
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It is a typical Data Science assignment. We have to answer few questions asked in the assignment like why do you choose the features? or where can you use this model?

Q1. Explain the project and project related questions
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Q2. Explain the thought process about the choosing the model in your assignment
Ans. 
The thought process for choosing the model involved considering the problem requirements, available data, and the desired outcome.
• Identified the problem requirements and objectives

• Explored the available data and its quality

• Considered the nature of the problem (classification, regression, etc.)

• Evaluated different models suitable for the problem

• Analyzed the strengths and weaknesses of each model

• Selected the model that be...read more
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Q3. Explain the EDA, features and metrics used in the assignment.
Ans. 
EDA involved exploratory analysis of data to identify patterns and insights. Features included demographic and behavioral data. Metrics used were accuracy, precision, recall, and F1 score.
• EDA involved data cleaning, visualization, and statistical analysis

• Features included age, gender, income, education, and purchase history

• Metrics used were accuracy, precision, recall, and F1 score to evaluate model performance

• Explorat...read more
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Q1. Few one-word answer questions to test your fitment in the company
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Q2. Opinion based questions - for example do you think that AI is used extensively in current world? and do we need to privatize the every data we gather?
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Q1. What are your salary expectations?
Ans. 
I expect a competitive salary based on my experience, skills, and the market rate for data scientists.
• I have researched the average salary range for data scientists in the industry.

• I have considered my level of experience and expertise in the field.

• I am open to discussing the salary package based on the overall compensation package offered by the company.

• I value fair compensation that aligns with the responsibilities a
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Q2. Share details of your previous job.
Ans. 
I worked as a Data Scientist at XYZ company.
• Developed machine learning models to predict customer churn.

• Analyzed large datasets to identify patterns and trends.

• Collaborated with cross-functional teams to develop data-driven solutions.

• Implemented data visualization techniques to communicate insights to stakeholders.
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Q3. Salary negotiations was discussed
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