Heliware Interview Questions and Answers

I was given 4 questions and asked to share the response in a presentation format for them

Q1. I was primarily asked about my experiences, and a little bit of cultural fitment
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Q1. This was a discussion round with the founder about the role
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Q1. This was a F2F round with the Founders and the Head of HR, with the intention being to understand a little bit more about my experiences and how i might fit the team
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Q1. Company profile
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Q2. What is travel and tourism
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Q1. What is a dmc?
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Q2. Disadvantages faced by a dmc
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Q1. Criteria based questions
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Q2. Situation handling questions
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Q1. Introduction yourself in details covering each points
Ans. 
Experienced cluster manager with a proven track record in team leadership and strategic planning.
• Over 5 years of experience in managing multiple teams and projects simultaneously

• Strong leadership skills with the ability to motivate and inspire team members

• Expertise in strategic planning and execution to achieve organizational goals

• Excellent communication and interpersonal skills for effective team collaboration

• Proven t...read more
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Q2. Location can be for new hotels for max revenue
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Q1. Talk about your work experience. Detailed questions about projects that I worked on.
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Q2. Why are you interested in switching.
Ans. 
Seeking new challenges and growth opportunities in a dynamic field.
• Interested in leveraging my skills and experience in a new environment

• Excited about the potential to work on innovative products

• Looking for opportunities to expand my knowledge and expertise
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Q3. Do you use any food delivery apps frequently? What would you improve in Swiggy?
Ans. 
Yes, I frequently use food delivery apps like Swiggy.
• I use Swiggy at least a few times a week for ordering food.

• I appreciate the wide variety of restaurants and cuisines available on Swiggy.

• Improvement suggestion: Enhance the user interface for a smoother ordering experience.

• Improvement suggestion: Implement a more accurate delivery time estimation system.

• Improvement suggestion: Offer more personalized recommendations
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Q4. What is northstar metric, what would be northstar metric for Swiggy
Ans. 
Northstar metric is the key metric that drives the core value of a product. For Swiggy, the northstar metric could be Daily Active Users (DAU) or Orders per User.
• Northstar metric is the single metric that best captures the core value that your product delivers to customers.

• For Swiggy, the northstar metric could be Daily Active Users (DAU) as it represents the number of unique users who engage with the platform on a dai...read more
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Q1. Explain what you worked on, how did you grow the adoption of the feature.
Ans. 
I worked on improving the adoption of a new feature by conducting user research, implementing user feedback, and creating targeted marketing campaigns.
• Conducted user research to understand user needs and preferences

• Implemented user feedback to make necessary improvements to the feature

• Created targeted marketing campaigns to promote the feature to the right audience
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Q2. Design product to manage risks during an event. Sub-question- How would this change if the target market is tier 3 cities in India
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A random topic was given and we had to speak on it

Q1. Why do u want to join us
Ans. 
I am passionate about data science and believe your company offers a great opportunity for growth and learning.
• Passionate about data science

• Believe company offers growth and learning opportunities
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Q2. A guestimate question, how many tea cups are consumed daily in delhi
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Q1. What is Bias In ML?
Ans. 
Bias in ML refers to the systematic error in a model's predictions, leading to inaccurate results.
• Bias is the algorithm's tendency to consistently learn the wrong thing by not taking all factors into account.

• It can result from the data used to train the model being unrepresentative or skewed.

• Bias can lead to unfair or discriminatory outcomes, especially in sensitive areas like hiring or lending decisions.

• Examples inclu...read more
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Q2. Regression concept in ML
Ans. 
Regression in ML is a supervised learning technique used to predict continuous values based on input features.
• Regression models establish a relationship between dependent and independent variables.

• Common regression algorithms include linear regression, polynomial regression, and logistic regression.

• Evaluation metrics for regression models include Mean Squared Error (MSE) and R-squared.

• Example: Predicting house prices b...read more
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50 question in 15 mins

Q1. On site evaluation, where various tasks were given
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Q2. Why do u want to join us
Ans. 
I am passionate about data science and believe your company offers a great opportunity for growth and learning.
• I am passionate about data science and enjoy working with data to derive insights and make informed decisions.

• I believe your company is at the forefront of innovation in the field of data science, and I am excited about the opportunity to learn from experts in the industry.

• I am impressed by the projects your c...read more
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