Data Scientist – Advanced Analytics & AI

About the role

As a Data Scientist at KPMG Luxembourg you will apply advanced analytics, statistical modelling, machine learning and data visualisation to turn complex financial data into actionable insights. You will collaborate with business specialists, data engineers and solution leads to support clients and internal teams with data‑driven decision making.

Key responsibilities

• Build, validate and maintain machine‑learning models for risk detection, anomaly identification and process optimisation.
• Apply statistical techniques to uncover trends, patterns and business insights.
• Evaluate model performance, ensuring explainability, transparency and robustness.
• Research and integrate emerging AI and generative AI technologies relevant to KPMG services.
• Leverage data centralisation in the Datalake/Microsoft Fabric to create scalable data models.
• Optimise data‑quality rules and use predictive modelling to improve data organisation.
• Develop reusable analytics assets and accelerators.
• Document methodologies, assumptions and analytical procedures.

Required profile

• Master’s degree or equivalent in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative field.
• Additional certifications in Data Science, AI, Cloud Technologies or Analytics are a plus.
• Strong knowledge of Python and its data‑science libraries.
• Experience with SQL and large‑scale data extraction and transformation.
• Solid understanding of statistical analysis and machine‑learning techniques.
• Hands‑on experience with Azure cloud services, including Azure Data Factory.

Required skills

• Python
• SQL
• Machine learning
• Statistical analysis
• Data visualisation
• Azure Data Factory
• Azure cloud services
• Microsoft Fabric (Data Lake)

What we offer

• Flexible work model (onsite, remote or hybrid options).
• Competitive compensation package and recognition bonuses.
• Paid time off and well‑being programs.
• Personal development and continuous learning opportunities.
• Engaging events and a supportive community.