Job Title : Data Scientist - Forecasting & Scenario Analysis

Key Responsibilities :

Solution Design & Ideation :

- Design and conceptualize advanced forecasting and predictive analytics solutions to address complex business challenges.

- Identify opportunities to apply machine learning, time-series modeling, and simulation to improve decision-making in areas such as release planning, budgeting, and talent payout calculations.

- Translate business objectives into analytical frameworks, ensuring clarity on impact, feasibility, and approach.

Solution Execution & Delivery :

- Develop, train, and validate predictive and forecasting models using diverse datasets (financial data, release schedules, marketing spend, sentiment signals, and industry benchmarks).

- Build and maintain end-to-end ML pipelines, ensuring reproducibility, scalability, and accuracy.

- Collaborate with engineers, product managers, and architects to integrate models into media finance workflows and decision-support platforms.

- Apply explainability methods (e.g., SHAP, feature importance) to ensure outputs are transparent and trusted by stakeholders.

Client Engagement & Stakeholder Management :

- Partner with stakeholders across Finance, Distribution, Strategy, and Content to align data science solutions with business goals in entertainment domains.

- Present analytical findings, forecasts, and scenario outcomes in a clear, compelling manner to both technical and non-technical audiences.

- Act as a trusted advisor on predictive modelling and forecasting, contextualizing solutions for executives making film release, budgeting, or marketing decisions.

Analytics & Data Expertise :

- Apply advanced statistical, time-series, and machine learning techniques to generate accurate forecasts and actionable insights.

- Engineer features and integrates large-scale, multi-source datasets, including structured ERP/financial data and unstructured signals such as social sentiment, marketing activity, and competitor releases.

- Monitor, retrain, and refine models based on new data, feedback, and evolving market conditions.

- Ensure adherence to governance, compliance, and data quality standards.

Team Collaboration & Development :

- Work collaboratively within a cross-functional pod including designers, AI consultants, and data architects.

- Mentor junior data professionals and contribute to knowledge sharing and best practices.

- Support agile ways of working through sprint planning, backlog reviews, and governance checkpoints.

Technology Acumen :

- Strong proficiency in Python/R/SQL and ML libraries (Scikit-learn, Statsmodels, Prophet, XGBoost, PyTorch/TensorFlow).

- Familiarity with cloud ML environments (AWS SageMaker, Azure ML, Databricks, or GCP Vertex AI) for scalable deployments.

- Awareness of BI/visualization tools (Power BI, Tableau, Looker) to communicate insights effectively.

- Understanding of MLOps practices for reproducibility, monitoring, and continuous improvement.

Qualifications :

- Master's or higher degree in Data Science, Statistics, Applied Mathematics, Computer Science, or related field.

- 6-8 years of hands-on data science experience, with demonstrated expertise in forecasting, predictive modeling, or scenario analysis.

- Experience handling large, multi-source datasets, including both structured and unstructured data.

- Proven ability to translate data-driven insights into business value across industries, with exposure to media finance or entertainment analytics highly desirable.

- Strong communication skills to present analytical outputs in business-friendly narratives for executives, planners, and creative teams.

- Exposure to cloud-based ML environments (AWS, Azure, GCP, Databricks) is preferred.

- Comfortable working in cross-functional, agile teams with multiple stakeholders.

- Familiarity with BI/visualization tools (Tableau, Power BI, Looker) for communicating model outputs.