Interview Question for Data Scientist II at Swiggy
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Behavioral/Fit Interview Question:
Tell me about a time when you had to explain a technical concept to a non-technical team member or stakeholder. How did you approach the situation and what strategies did you use to ensure effective communication?In my previous job, I was working on a project that involved incorporating a new software into our company's system. However, some of my team members were non-technical and struggled to understand the technical concepts involved in the implementation process. To successfully communicate the technical details, I approached the situation in the following ways:

  • Firstly, I made sure to gauge the team members' initial understanding of the software and problem domain. This helped me identify knowledge gaps that needed to be bridged.
  • Then, I simplified the technical information into easily understandable terms that could be related to the product's end goals. I used analogies and visual aids where needed to make the most complex technical aspects accessible to my non-technical team members.
  • I also made an effort to actively listen and address questions and concerns as they came up. This gave the non-technical team members confidence that they could fully grasp the technical details, even if they required further explanation.
  • Finally, I confirmed that the team members understood the technical concepts by asking them to reiterate the main points of the explanation in their own words.

Using these strategies, I was able to help the team members understand the technical aspects of the project, which in turn led to the successful integration of the software into the company's system.Technical Interview Question:
Suppose you are given a dataset of customer food orders at Swiggy and you are asked to predict which customer is likely to place an order again in the next week. How would you approach this problem and what techniques or algorithms would you use? for Data Scientist II role at Swiggy.To tackle this problem, I would use a combination of exploratory data analysis (EDA) and predictive modeling techniques. This would involve the following steps:

  • Firstly, I would conduct data cleaning to address missing values and outliers and ensure the dataset is in a usable format.
  • Next, I would perform EDA to gain insights into the data, identify patterns, and relationships between features to pinpoint any correlations.
  • After gaining insights, I would identify appropriate predictive modeling algorithms that would best suit the problem and data available, such as Random Forest, KNN or Naive Bayes Classifier.
  • I would then train the selected algorithms on the preprocessed data.
  • After training, I would use cross-validation techniques such as K-fold cross-validation to evaluate the performance of the models.
  • Finally, I would select the best model based on its performance and use it to predict which customer is likely to place an order again in the next week.

In conclusion, utilizing EDA and machine learning techniques would assist in resolving this problem. Citations:

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