Siemens Data Scientist Interview Questions and Answers

Basics and Fundamentals

Q1. Reason for job switch
Ans. 
Seeking new challenges and opportunities for growth in a different environment.
• Looking for a more challenging role to further develop skills

• Interested in exploring new technologies and methodologies

• Seeking a better work-life balance or company culture

• Opportunity for career advancement or higher salary

• Desire for a change in industry or focus area
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Q2. Work Culture Expectations
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Basic college level, basic logic based questions and some mathematical questions like speed Avg questions

Q1. What is the difference between AI, deep learning and machine learning
Ans. 
AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart. Machine learning is a subset of AI that allows machines to learn from data. Deep learning is a subset of machine learning that uses neural networks with many layers to model and solve complex problems.
• AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart.

• Machine ...read more
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Basics and Fundamentals

Q1. Reason for job switch
Ans. 
Seeking new challenges and opportunities for growth in a different environment.
• Looking for a more challenging role to further develop skills

• Interested in exploring new technologies and methodologies

• Seeking a better work-life balance or company culture

• Opportunity for career advancement or higher salary

• Desire for a change in industry or focus area
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Q2. Work Culture Expectations
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Q1. Why do you like data?
Ans. 
I like data because it allows me to uncover insights, make informed decisions, and solve complex problems.
• Data helps me understand trends and patterns in various industries.

• I enjoy using data to drive business strategies and improve processes.

• Analyzing data allows me to make evidence-based decisions and solve real-world problems.

• Data visualization helps me communicate findings effectively to stakeholders.
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Q2. Why do you want to join Honeywell?
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Q3. Where do you see yourself in 5 yrs?
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Basic college level, basic logic based questions and some mathematical questions like speed Avg questions

Q1. 1. How would you deal with time series data
Ans. 
Time series data can be analyzed using techniques like smoothing, decomposition, forecasting, and anomaly detection.
• Use smoothing techniques like moving averages to remove noise and identify trends.

• Apply decomposition methods like seasonal decomposition of time series (STL) to separate trend, seasonality, and residual components.

• Utilize forecasting models such as ARIMA, SARIMA, or Prophet to predict future values.

• Imple...read more
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Q2. 2. How would you handle missing data in time series data
Ans. 
Handle missing data in time series data by imputation, interpolation, or deletion.
• Impute missing values using mean, median, mode, or predictive modeling techniques.

• Interpolate missing values using linear interpolation, spline interpolation, or time-based interpolation.

• Delete rows with missing values if they are few and do not significantly impact the analysis.
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