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The goal is to train and develop a machine learning model to predict the likelihood of a user converting to a paying customer, excluding those who request a refund. The model will generate user-level predictions as soon as a user starts the trial. Voyantis trains its models using historical data from matured users and applies them to new users The Scenario: A customer operates a subscription-based streaming service (similar to Spotify) and wants to predict the likelihood of users converting to a paid subscription at the end of their 7-day free trial. Users may claim a refund if they have been charged and forgot to cancel their trial. There are two main user flows in our customer’s product: 1. Web-to-App Flow: ● Users begin their free trial by entering credit card information on the web. ● They then download the app and use it. ● If they do not cancel within 7 days, they are automatically charged. Historical data available for model creation: ● User demographic ● App engagement ● Payment data ● Refund data is missing (customer estimates a 20% refund rate). 2. App Flow: ● Users begin their trial by downloading the app directly and start using it. ● If they do not cancel, they are charged via the app store (iOS or Android) on the 7th day. Historical data for model creation: ● User demographic ● App engagement ● Payment data is available for iOS users only (they make up 85% of app users). ● No payment data is available for Android users. ● Refund data is available. Please provide a structured response addressing the following questions: A. Model Objective & Approach 1. What is the prediction period you would choose (how many days forward to predict from trial start) 2. What metrics should support your decision? 3. Would you build a one unified model for both flows, or separate models for Web-to-App and App Flow? What metrics should support your decision? B. Handling Data Limitations 4. How would you account for the missing refund data in the Web-to-App flow in your model training? 5. How would you handle the missing payment data for Android users in the App Flow? 6. What assumptions or adjustments would you make to compensate for these missing values? 3. Data Enhancement & Additional Features 7. What additional data could help improve the accuracy of the model? 4. Data Analysis
avatar

Technical Account Manager (TAM)

Interviewed at Voyantis

4.2★
Apr 6, 2025

The goal is to train and develop a machine learning model to predict the likelihood of a user converting to a paying customer, excluding those who request a refund. The model will generate user-level predictions as soon as a user starts the trial. Voyantis trains its models using historical data from matured users and applies them to new users The Scenario: A customer operates a subscription-based streaming service (similar to Spotify) and wants to predict the likelihood of users converting to a paid subscription at the end of their 7-day free trial. Users may claim a refund if they have been charged and forgot to cancel their trial. There are two main user flows in our customer’s product: 1. Web-to-App Flow: ● Users begin their free trial by entering credit card information on the web. ● They then download the app and use it. ● If they do not cancel within 7 days, they are automatically charged. Historical data available for model creation: ● User demographic ● App engagement ● Payment data ● Refund data is missing (customer estimates a 20% refund rate). 2. App Flow: ● Users begin their trial by downloading the app directly and start using it. ● If they do not cancel, they are charged via the app store (iOS or Android) on the 7th day. Historical data for model creation: ● User demographic ● App engagement ● Payment data is available for iOS users only (they make up 85% of app users). ● No payment data is available for Android users. ● Refund data is available. Please provide a structured response addressing the following questions: A. Model Objective & Approach 1. What is the prediction period you would choose (how many days forward to predict from trial start) 2. What metrics should support your decision? 3. Would you build a one unified model for both flows, or separate models for Web-to-App and App Flow? What metrics should support your decision? B. Handling Data Limitations 4. How would you account for the missing refund data in the Web-to-App flow in your model training? 5. How would you handle the missing payment data for Android users in the App Flow? 6. What assumptions or adjustments would you make to compensate for these missing values? 3. Data Enhancement & Additional Features 7. What additional data could help improve the accuracy of the model? 4. Data Analysis

Reverse a linked list. Implicit and explicit intents. different components of android. life cycle of a fragment. how add back to stack works in fragment. different ways to call services. different types of services. basics on socket programming.
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Technical Manager

Interviewed at Jio

4.4★
Dec 19, 2017

Reverse a linked list. Implicit and explicit intents. different components of android. life cycle of a fragment. how add back to stack works in fragment. different ways to call services. different types of services. basics on socket programming.

Most of the interview was focused on me sharing my professional experience to include my strategy as a technical recruiter and leader. Many of the conversations were open ended, but all of the necessary points were covered. It was very conversational which helped things flow nicely.
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Manager, Technical Recruiting

Interviewed at EverOps

4.2★
Jul 15, 2022

Most of the interview was focused on me sharing my professional experience to include my strategy as a technical recruiter and leader. Many of the conversations were open ended, but all of the necessary points were covered. It was very conversational which helped things flow nicely.

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