# Principal Data Scientist at Philips

Philips is hiring a Principal Data Scientist in Bengaluru, India. Level rates it Builds AI ●●●●; you can [apply on Philips](https://jobsbylevel.com/go/e6db676d-ec0d-42e0-b7a1-ba35aadf9003?utm_medium=ai-agent).

AI Level 4, AI centrality 85 out of 100. Bangalore.

## Details

- Company: [Philips](https://jobsbylevel.com/companies/philips)
- AI level: AI Level 4 (score 85 out of 100)
- Location: Bangalore
- Posted: November 12, 2026
- Apply: https://jobsbylevel.com/go/e6db676d-ec0d-42e0-b7a1-ba35aadf9003?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=principal-data-scientist-at-philips-3cab13

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## Description

Job Title Principal Data Scientist Job Description Job title: Principal Data Scientist Your role: The Performance Analytics & AI Business Partner combines deep commercial understanding with strong hands-on expertise in data science, machine learning, Generative AI and agentic AI. The role is accountable for turning complex performance data into trusted decisions, scalable analytics products and intelligent tools that help Business Units and Regions sense, diagnose, predict and act faster. The role owns the performance-analytics foundation: sell-out and market data from syndicated vendors such as Circana, GfK, Nielsen and IQVIA; performance dashboards; data definitions; and the business narrative behind the numbers. It also leads the evolution beyond traditional reporting by building advanced analytical models, AI agents and conversational decision-support tools. This is not a reporting role and not a purely technical data-scientist role. It requires someone who can build robust analytical solutions, understand the underlying data and models, and confidently influence senior commercial leaders with clear “so what / now what” recommendations. Job Responsibilities: 1. Performance Analytics Business Partnering Act as the day-to-day performance analytics partner for assigned Businesses and Regions. Translate leadership questions into clear analytics, data-science and AI solution requirements. Lead analysis across market share, sell-in, sell-out, revenue, margin, price, mix, distribution, customer and channel performance. Support business reviews, planning cycles, forecasts and strategic discussions with fact-based insights and recommendations. Connect multiple performance signals to identify underlying drivers, risks, opportunities and required actions. Challenge assumptions constructively and translate complex analysis into concise executive narratives. 2. Syndicated Data, Data Foundations and Governance Own the business use, interpretation and quality of syndicated market and sell-out data from Circana, GfK, Nielsen, IQVIA and other relevant vendors. Understand and manage data methodologies, coverage, definitions, assumptions and limitations. Work with Data & Technology, data engineering and external providers to integrate, validate and harmonise data in ADL and related platforms. Establish robust reconciliation, validation and quality-control processes to ensure performance outputs are trusted and first-time-right. Define common business rules, hierarchies, calculations and performance definitions across Businesses and Regions. Document data logic, methodologies and known limitations clearly. 3. Dashboards and Analytics Products Own and continuously improve sell-out and commercial performance dashboards as analytics products, not static reports. Build scalable, reusable dashboards and automated analytical workflows using SQL, Python and modern BI tools. Move dashboards beyond reporting by embedding automated diagnostics, alerts, benchmarks, root-cause analysis and recommended actions. Lead requirements gathering, design, user testing, deployment and adoption of analytics products. Simplify the dashboard landscape by eliminating duplicate, manual and low-value reporting. Monitor solution usage, user feedback, data quality and business impact, and continuously improve the product backlog. 4. Advanced Analytics and Data Science Analyse complex, high-volume structured and unstructured datasets using robust statistical and data-science methods. Develop and deploy predictive and diagnostic models for commercial performance questions, including forecasting, regression, classification, clustering, anomaly detection, price and mix analysis. Own the end-to-end data-science lifecycle: problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and improvement. Assess model performance, stability, explainability and business relevance before scaling solutions. Apply scientific rigour to

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