Selected intern's day-to-day responsibilities include:

1. Create and improve movie tags and their relevance score based on a multitude of factors and feature data sets
2. Improve the content-based filtering system
3. Build recommendation systems based on the different user personas to predict the right movie
4. Improve movie suggestions based on user inputs
5. Plug-in user feedback to create collaborate algorithms
6. Plug-in more relevant data collection resources to improve the accuracy of recommendation systems

Note: We are looking for folks who can improve our recommendation systems using machine learning algorithms. We have working models developed using various data sourcing, NLP techniques, and correlation algorithms. We are looking for folks who can take it forward by fine-tuning the algorithms and improving them to achieve higher accuracy in movie likeability prediction and increase user retention by telling them what movie or TV show to watch and also why we are recommending it to them.

Please apply only if you have worked earlier on at least one machine learning project implemented in a real-life scenario, and are well versed with a wide range of Scikit/Pandas kind of libraries.

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