Overview

Design and deploy scalable forecasting models to predict product demand.

Responsibilities

• Demand Forecasting: Design, build, and deploy scalable demand forecasting models (time-series, ML-based) to predict product demand at SKU, category, channel, and regional levels.
• Discount & Price Simulation: what-if simulation tools to optimize discount strategies and maximize margin.
• End-to-End Model Ownership: Own the full ML lifecycle—data exploration, feature engineering, model training, validation, deployment, monitoring, and iteration.
• Analyzes multifaceted and high-dimensional data problems, developing innovative solutions, formulating sophisticated hypotheses, and creating advanced proof of concepts to validate and refine analytical models.
• Ensures the highest standards of data quality, accuracy, and reliability by designing and implementing rigorous validation protocols, advanced data cleansing techniques, and comprehensive quality control measures, working under limited supervision.
• Participates in end-to-end data mining projects, utilizing advanced algorithms, machine learning models, and AI techniques to extract deep and actionable insights from complex, large-scale data sources.
• Executes the deployment and rigorous testing of data science solutions and insights, ensuring optimal performance, scalability, and seamless integration with existing enterprise systems and workflows.
• Maintains robust, scalable data pipelines and workflows, leveraging cutting-edge big data technologies and database management systems to support advanced analytics and machine learning projects.
• Develops, documents, and disseminates comprehensive methodologies, processes, and analytical findings, ensuring transparency, reproducibility, and facilitating cross-functional knowledge sharing and collaboration.
• Evaluates and implements state-of-the-art machine learning and AI techniques, continuously researching and applying novel approaches to solve highly complex business problems and drive strategic innovation within Philips.
• Presents complex data-driven insights and strategic recommendations to senior stakeholders, effectively translating intricate analytical results into clear, actionable business strategies and influencing key decision-making processes.
• Monitors, maintains, and continuously improves the performance of deployed models, conducting regular reviews, updates, and optimizations to ensure sustained accuracy, relevance, and business impact.
• Interacts and collaborates with cross-functional teams, including IT, data engineering, and various business units, to ensure the successful operationalization, integration, and scaling of data science solutions across the organization.
• Data Analysis & Interpretation
• Data Governance
• Statistical Methods
• Statistical Programming Software
• Business Intelligence Tools
• Data Mining
• Machine Learning Engineering Fundamentals
• Research & Analysis
• Minimum required Education:
• Bachelor's / Master's Degree in Computer Science, Econometrics, Artificial Intelligence, Applied Mathematics, Statistics or equivalent.
• Minimum required Experience:
• Minimum 6 years of experience with Bachelor's in areas such as Data Analytics, Data Science, Data Mining, Artificial Intelligence, Pattern Recognition or equivalent.

Qualifications

• Root Cause Analysis (RCA)
• Data Quality Management Systems
• Regulatory Compliance
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Nice to have

• Experience with statistical programming
• Knowledge of data governance

Education

• Bachelor's Degree in Computer Science or related field

Benefits

• Competitive salary
• Career growth opportunities