Data Scientist – Performance Analytics

About the role

The Senior Data Scientist – Performance Analytics combines hands‑on data science, analytics‑product development and commercial understanding to help Philips monitor, explain and predict business performance. The role partners with business, regional and functional teams and owns analytics workstreams from problem framing through deployment and adoption.

Key responsibilities

• Translate business priorities into analytical requirements, analyze market share, revenue, margin and operational performance, and communicate findings with actionable recommendations.
• Prepare, integrate and validate data from internal and external sources using Python, SQL and enterprise data platforms; collaborate with Data Engineering to improve trusted datasets.
• Develop, validate and monitor machine‑learning models (regression, classification, clustering, forecasting, anomaly detection) and contribute to AI‑enabled solutions such as automated commentary and conversational analytics.
• Productionize analytical solutions with clean, modular code, version control and testing; support user acceptance testing, deployment and issue resolution.
• Drive adoption through demonstrations, training, documentation and stakeholder engagement; share methods and coach junior colleagues.

Required profile

• Approximately 4‑7 years of experience in data science, advanced analytics, commercial analytics or business intelligence.
• Bachelor's or Master's degree in Computer Science, Data Science, Econometrics, AI, Applied Mathematics, Statistics, Engineering, Business Analytics or a related quantitative discipline.
• Strong commercial and financial understanding with proven problem‑solving and root‑cause analysis skills.
• Ability to independently manage analytics workstreams and collaborate across Business, Analytics, IT, Finance and Data Engineering teams.

Required skills

• Python and SQL for data preparation, exploratory analysis and statistical modelling.
• Machine‑learning techniques including regression, classification, clustering, forecasting and anomaly detection.
• Business‑intelligence/visualisation tools such as Power BI or Qlik.
• Experience with cloud data platforms (Azure, Databricks, ADL) and enterprise analytics environments.
• Version control (e.g., Git), testing, documentation and reproducible analytical‑development practices.