Sigmoid Senior Data Scientist Lead Interview Questions and Answers

Q1. How will you find loyal customers for a store like DMart , SmartBazar
Ans. 
Utilize customer transaction data and behavior analysis to identify loyal customers for DMart and SmartBazar.
• Use customer transaction history to identify frequent shoppers

• Analyze customer behavior patterns such as repeat purchases and average spend

• Implement loyalty programs to incentivize repeat purchases

• Utilize customer feedback and reviews to gauge loyalty

• Segment customers based on their shopping habits and preferenc
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Q2. Who is more valuable a customer who is making small transactions everyday or the customer who makes big transactions in a month
Ans. 
It depends on the business model and goals of the company.
• Small transactions everyday can lead to consistent revenue streams and customer engagement.

• Big transactions in a month can indicate high purchasing power and potential for larger profits.

• Consider customer lifetime value, retention rates, and overall business strategy when determining value.
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Q3. What will you do as a data scientist if the sales of a store is declining
Ans. 
I would conduct a thorough analysis of the sales data to identify trends and potential causes of the decline.
• Review historical sales data to identify patterns or seasonality

• Conduct customer surveys or interviews to gather feedback

• Analyze competitor data to understand market dynamics

• Implement predictive modeling to forecast future sales

• Collaborate with marketing team to develop targeted strategies
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Q4. We have to bundle the items together in the units of 2-3 as a single units like chips of 3 packets together. how to identify which items to bundle and number of units. Create a machine learning model for i...read more
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Q5. You are working in a project, where your approach towards problem is more innovative while the rest of the team is following conventional approach. how will you convince them to follow your approach.
Ans. 
I would showcase the potential benefits and results of my innovative approach to convince the team.
• Highlight the advantages of the innovative approach such as improved efficiency, accuracy, or cost-effectiveness.

• Provide real-world examples or case studies where similar innovative approaches have led to successful outcomes.

• Encourage open discussion and collaboration within the team to explore the potential of combining ...read more
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1. A store has promotional offers how will you analyse that offers are working in their favour.
2. What data will you require if you want to predict the sales of the chocolate in a store.
3. Why data is distributed normally in linear regression.
4. Difference between linear and logistic regression
5. A person who is senior to you and you are working on the same project. But that person has very bad reputation of misbehaving and being rude to people. And he is doing same with you. What will you do?

Q1. Explain any ML model.
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Q2. Create Dataframe from two lists.
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Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
Ans. 
Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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SQL, Python coding …

Basic mathematical and resoning questions.

Q1. Tell me about your self
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Q2. Explain the recent project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest

• Evaluated model performance using metrics like accuracy and AUC-ROC curve
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Q3. What is the difference between random forest and decision tree
Ans. 
Random forest is an ensemble learning method that uses multiple decision trees to make predictions, while a decision tree is a single tree-like structure that makes decisions based on features.
• Random forest is a collection of decision trees that work together to make predictions.

• Decision tree is a single tree-like structure that makes decisions based on features.

• Random forest reduces overfitting by averaging the predic...read more
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Q4. What is cost function
Ans. 
A cost function is a mathematical formula used to measure the cost of a particular decision or set of decisions.
• Cost function helps in evaluating the performance of a model by measuring how well it is able to predict the outcomes.

• It is used in optimization problems to find the best solution that minimizes the cost.

• Examples include mean squared error in linear regression and cross-entropy loss in logistic regression.
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Q1. Sql, Python programming Questions
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Q1. Retail, CPG based case study questions like offer allocation method for loyal customers
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I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
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Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
Ans. 
Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
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