Job Description
<p>Job Title : Data Scientist - Forecasting & Scenario Analysis<br/><br/><b>Key Responsibilities :</b><br/><br/><b>Solution Design & Ideation :</b><br/><br/>- Design and conceptualize advanced forecasting and predictive analytics solutions to address complex business challenges.<br/><br/>- 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.<br/><br/>- Translate business objectives into analytical frameworks, ensuring clarity on impact, feasibility, and approach.<br/><br/><b>Solution Execution & Delivery :</b><br/><br/>- Develop, train, and validate predictive and forecasting models using diverse datasets (financial data, release schedules, marketing spend, sentiment signals, and industry benchmarks).<br/><br/>- Build and maintain end-to-end ML pipelines, ensuring reproducibility, scalability, and accuracy.<br/><br/>- Collaborate with engineers, product managers, and architects to integrate models into media finance workflows and decision-support platforms.<br/><br/>- Apply explainability methods (e.g., SHAP, feature importance) to ensure outputs are transparent and trusted by stakeholders.<br/><br/><b>Client Engagement & Stakeholder Management :</b><br/><br/>- Partner with stakeholders across Finance, Distribution, Strategy, and Content to align data science solutions with business goals in entertainment domains.<br/><br/>- Present analytical findings, forecasts, and scenario outcomes in a clear, compelling manner to both technical and non-technical audiences.<br/><br/>- Act as a trusted advisor on predictive modelling and forecasting, contextualizing solutions for executives making film release, budgeting, or marketing decisions.<br/><br/><b>Analytics & Data Expertise :</b><br/><br/>- Apply advanced statistical, time-series, and machine learning techniques to generate accurate forecasts and actionable insights.<br/><br/>- 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.<br/><br/>- Monitor, retrain, and refine models based on new data, feedback, and evolving market conditions.<br/><br/>- Ensure adherence to governance, compliance, and data quality standards.<br/><br/><b>Team Collaboration & Development :</b><br/><br/>- Work collaboratively within a cross-functional pod including designers, AI consultants, and data architects.<br/><br/>- Mentor junior data professionals and contribute to knowledge sharing and best practices.<br/><br/>- Support agile ways of working through sprint planning, backlog reviews, and governance checkpoints.<br/><br/><b>Technology Acumen :</b><br/><br/>- Strong proficiency in Python/R/SQL and ML libraries (Scikit-learn, Statsmodels, Prophet, XGBoost, PyTorch/TensorFlow).<br/><br/>- Familiarity with cloud ML environments (AWS SageMaker, Azure ML, Databricks, or GCP Vertex AI) for scalable deployments.<br/><br/>- Awareness of BI/visualization tools (Power BI, Tableau, Looker) to communicate insights effectively.<br/><br/>- Understanding of MLOps practices for reproducibility, monitoring, and continuous improvement.<br/><br/><b>Qualifications :</b><br/><br/>- Master's or higher degree in Data Science, Statistics, Applied Mathematics, Computer Science, or related field.<br/><br/>- 6-8 years of hands-on data science experience, with demonstrated expertise in forecasting, predictive modeling, or scenario analysis.<br/><br/>- Experience handling large, multi-source datasets, including both structured and unstructured data.<br/><br/>- Proven ability to translate data-driven insights into business value across industries, with exposure to media finance or entertainment analytics highly desirable.<br/><br/>- Strong communication skills to present analytical outputs in business-friendly narratives for executives, planners, and creative teams.<br/><br/>- Exposure to cloud-based ML environments (AWS, Azure, GCP, Databricks) is preferred.<br/><br/>- Comfortable working in cross-functional, agile teams with multiple stakeholders.<br/><br/>- Familiarity with BI/visualization tools (Tableau, Power BI, Looker) for communicating model outputs.<br/></p> (ref:hirist.tech)