Bazaarvoice Data Scientist Interview Questions and Answers

Q1. The Interviewer displayed a block of Deep Learning code and asked me to explain it
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Q1. Tell me about yourself
Ans. 
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in analyzing large datasets

• Skilled in programming languages like Python and R

• Strong problem-solving skills

• Passionate about leveraging data to drive insights and decision-making
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Q2. Why consulting?
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Consulting allows me to work on a variety of projects, industries, and challenges, providing valuable experience and exposure.
• Opportunity to work on diverse projects and industries

• Exposure to different challenges and problem-solving scenarios

• Ability to apply expertise in various contexts

• Opportunity to collaborate with different teams and clients
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Test was conducted on skills like Advanced excel, MySQL, Analytical skills and understanding of business domain. The test was time bound and with camera on.

Q1. They questions on projects done by me and my past relevant experience. Also, they asked technical questions regarding Python, MySQL, Tableau, Advanced Excel and also tried to understand why am I changing t...read more
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Q1. Questions on Statistics and Deep Learninh
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Q1. Tell me about yourself
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Q2. Tell me 3 strengths and 3 weaknesses
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Q1. What languages for ML do you know?
Ans. 
I am proficient in Python, R, and SQL for machine learning.
• Python

• R

• SQL
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Assignment was related to what the company works on. It was a take home assignment and updates regarding the same were followed up in the two other rounds of interview.

Q1. Updates regarding optimising current algorithm to fit the business model. Discussion with CEO regarding the role and work profile.
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Q2. How do you identify anomalies in a specific area of operation?
Ans. 
Anomalies can be identified by analyzing data using statistical methods and machine learning algorithms.
• Collect and preprocess data from the area of operation

• Visualize the data to identify patterns and outliers

• Apply statistical methods such as mean, standard deviation, and Z-score to detect anomalies

• Use machine learning algorithms such as clustering, classification, and regression to identify anomalies

• Validate the resu...read more
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Q3. What does time-series decomposition represent and what are the different components stand for.
Ans. 
Time-series decomposition is a method to break down a time-series into its different components.
• Time-series decomposition breaks down a time-series into trend, seasonal, and residual components.

• Trend component represents the long-term pattern in the data.

• Seasonal component represents the periodic fluctuations in the data.

• Residual component represents the random fluctuations in the data.

• Decomposition can be done using a...read more
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Q1. Introduction, Tools knowledge, Project work brief, Salary Expectation
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Q1. Case Study based- parametric/non-parametric, approach of data science problem solving, tools/tech overview but mostly testing sense of statistics.
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Q1. Head HR meeting - Some behavioral questions but mostly Introducing the organization and its work.
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