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We are seeking a Data Scientist (Machine Learning) to develop an advanced forecasting model for Premium Video on Demand (PVOD) and Premium Electronic Sell-Through (PEST) at the film title level. This is a high-impact, short-term project focused on improving forecasting accuracy by incorporating rich datasets beyond traditional box office performance. The ideal candidate will have hands-on experience in machine learning model development, forecasting techniques, and feature engineering, along with the ability to communicate insights to non-technical stakeholders.
Job Responsibility:
Develop and implement machine learning models to forecast consumer demand and spending
Incorporate diverse features such as marketing, film quality, competition, and pricing
Support both early-stage and post-theatrical forecasting models
Ensure model explainability for business stakeholders
Analyze historical datasets including performance, audience metrics, and competitive landscape
Deliver model outputs, accuracy assessments, and recommendations within tight timelines
Provide guidance for integration into weekly forecasting workflows
Requirements:
1–2 years of experience in machine learning model development
Strong understanding of forecasting methods and statistical techniques
Proficiency in Python or R
Experience with data analysis and feature engineering
Ability to work with large historical datasets and diverse data sources
Strong problem-solving and analytical thinking
Ability to explain complex models to non-technical stakeholders
High attention to detail and accuracy
Nice to have:
Experience in the entertainment industry or forecasting for PVOD/PEST
Knowledge of marketing impact, film performance, and competitive dynamics
Familiarity with early-stage and post-theatrical forecasting models