{"id":4178223,"url":"https://alion.io/job/philips-principal-data-scientist","title":"Principal Data Scientist","company":{"id":13838,"name":"Philips","domain":"philips.com","url":"https://alion.io/company/philips-2","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":87,"open_postings":81,"ghost_share":0,"stale_share":0.728,"repost_share":0,"time_to_fill_p50_days":17,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-12T16:42:27Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-13T15:07:05Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Title\nPrincipal Data ScientistJob Description\nJob title:\nPrincipal Data Scientist\nYour role:\nThe 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.\nThe 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.\nThis 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.\nJob Responsibilities:\n1. Performance Analytics Business Partnering\nAct as the day-to-day performance analytics partner for assigned Businesses and Regions.\nTranslate leadership questions into clear analytics, data-science and AI solution requirements.\nLead analysis across market share, sell-in, sell-out, revenue, margin, price, mix, distribution, customer and channel performance.\nSupport business reviews, planning cycles, forecasts and strategic discussions with fact-based insights and recommendations.\nConnect multiple performance signals to identify underlying drivers, risks, opportunities and required actions.\nChallenge assumptions constructively and translate complex analysis into concise executive narratives.\n2. Syndicated Data, Data Foundations and Governance\nOwn the business use, interpretation and quality of syndicated market and sell-out data from Circana, GfK, Nielsen, IQVIA and other relevant vendors.\nUnderstand and manage data methodologies, coverage, definitions, assumptions and limitations.\nWork with Data & Technology, data engineering and external providers to integrate, validate and harmonise data in ADL and related platforms.\nEstablish robust reconciliation, validation and quality-control processes to ensure performance outputs are trusted and first-time-right.\nDefine common business rules, hierarchies, calculations and performance definitions across Businesses and Regions.\nDocument data logic, methodologies and known limitations clearly.\n3. Dashboards and Analytics Products\nOwn and continuously improve sell-out and commercial performance dashboards as analytics products, not static reports.\nBuild scalable, reusable dashboards and automated analytical workflows using SQL, Python and modern BI tools.\nMove dashboards beyond reporting by embedding automated diagnostics, alerts, benchmarks, root-cause analysis and recommended actions.\nLead requirements gathering, design, user testing, deployment and adoption of analytics products.\nSimplify the dashboard landscape by eliminating duplicate, manual and low-value reporting.\nMonitor solution usage, user feedback, data quality and business impact, and continuously improve the product backlog.\n4. Advanced Analytics and Data Science\nAnalyse complex, high-volume structured and unstructured datasets using robust statistical and data-science methods.\nDevelop and deploy predictive and diagnostic models for commercial performance questions, including forecasting, regression, classification, clustering, anomaly detection, price and mix analysis.\nOwn the end-to-end data-science lifecycle: problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and improvement.\nAssess model performance, stability, explainability and business relevance before scaling solutions.\nApply scientific rigour to distinguish correlation, decomposition and causal insight; ensure models are used appropriately in decision-making.\nWrite clean, documented and reusable code, and partner with technical teams to productionise analytical solutions.\n5. AI, GenAI and Agentic Decision Intelligence\nDesign, build and deploy AI-enabled tools, Generative AI applications and analytical agents for performance management.\nUse LLMs, retrieval-based approaches, machine learning and automation to enable users to ask business questions in natural language and receive trusted, grounded answers.\nCreate agents that can monitor performance, identify deviations, retrieve relevant context, diagnose likely drivers, simulate scenarios and recommend next-best actions.\nCombine dashboards, predictive models and conversational AI into intuitive decision-support experiences.\nRapidly prototype AI use cases, test them with users and scale successful solutions into robust products.\nApply responsible-AI principles, including data security, access control, traceability, explainability, evaluation, bias awareness and clear human decision rights.\nStay current on developments in AI, LLMs, agentic frameworks and data science, applying innovations pragmatically where they create measurable value.\n6. Technology, Deployment and Solution Architecture\nPartner with IT, data engineering, software development and platform teams to ensure analytical and AI solutions are secure, scalable, reliable and aligned with enterprise standards.\nDefine technical requirements, support solution architecture, testing and production deployment.\nEstablish appropriate model monitoring, version control, documentation and evaluation processes.\nResolve data and technical issues that affect the reliability, speed or usability of analytics products.\nBalance rapid MVP delivery with sound product-engineering fundamentals.\n7. Adoption and Capability Building\nDrive adoption of dashboards, models and AI tools through training, coaching, clear playbooks and active engagement with business users.\nBuild confidence in data, analytical methods and AI recommendations by making logic and limitations transparent.\nHelp stakeholders use performance insight consistently in business reviews, planning, customer discussions and commercial decisions.\nShare best practices across Businesses, Regions and analytics teams.\nTrack whether insights and AI tools lead to decisions, actions and measurable business impact.\nSuccess Measures\nTrusted, consistent and timely sell-out and market-performance insights across Businesses and Regions.\nHigh adoption and demonstrable impact of dashboards, analytics products and AI-enabled tools.\nReduced manual reporting and duplicate analytics solutions.\nEarlier identification of performance risks and growth opportunities.\nIncreased use of predictive analytics, AI agents and automated diagnostics in regular business routines.\nStrong data quality, governance, model reliability and responsible-AI compliance.\nClear evidence that analytical recommendations translate into business decisions and commercial impact.\nPositive feedback from senior Business, Regional, Sales, Marketing and Finance stakeholders.\nYou're the right fit if:\nExperience\nPreferably 9+ years of experience in performance analytics, commercial analytics, data science, decision intelligence, business intelligence or a related field.\nProven experience with syndicated market or sell-out data, such as Circana, GfK, Nielsen, IQVIA or equivalent.\nDemonstrated experience building and deploying analytics products, dashboards, predictive models or AI-enabled tools-not only consuming reports.\nStrong experience partnering with senior commercial stakeholders and influencing decisions without direct authority.\nExperience working across multiple markets, categories or Business Units in a complex matrix organisation.\nExperience taking analytical solutions from business problem through build, deployment, adoption and measurable impact.\nTechnical and Analytical Expertise\nExpert-level hands-on capability in Python and SQL, with strong foundations in statistics, data modelling and data quality.\nStrong practical expertise in machine learning, including regression, forecasting, classification, clustering, anomaly detection and model evaluation.\nStrong working expertise in Generative AI, LLMs, prompt design, retrieval-augmented generation, AI-agent design and evaluation.\nExperience building AI or GenAI applications, ideally including conversational analytics, intelligent assistants or agentic workflows.\nExperience with BI and visualisation tools, cloud data platforms, data lakes/ADL and enterprise data environments.\nUnderstanding of data governance, model governance, responsible AI, deployment and monitoring practices.\nBusiness and Personal Skills\nStrong commercial and financial acumen, with the ability to connect analytical findings to performance, decisions and actions.\nAbility to turn complex data science and AI outputs into simple, compelling executive stories.\nHigh ownership and a builder mindset; comfortable moving rapidly from idea to MVP to scaled solution.\nCurious, pragmatic and rigorous-able to use advanced technology without over-engineering.\nStrong stakeholder management, communication and influencing skills.\nEducation\nBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Econometrics, Engineering, Mathematics or another quantitative discipline.\nAdditional education or demonstrated hands-on expertise in machine learning, GenAI, LLMs or AI-product development is strongly preferred.\nHow we work together\nWe believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.\nAbout Philips\nAre you ready to do the work of your life to help the lives of others? Learn more about our business, discover our rich and exciting history and learn more about our purpose.\nIf you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. 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