Overview The Data Science Senior Analyst (Lead Data Scientist) is part of the Data Impact Benelux team. This team is responsible for supporting the Benelux commercial data strategy and ecosystem through developing and implementing advanced analytics solution and a comprehensive business intelligence roadmap. The team works closely with the marketing & commercial ecosystem to ensure all commercial data are leveraged in the most optimal way. Responsibilities As the lead data scientist, this individual will be responsible for development and deployment of data science modeling frameworks using Statistical modeling (Machine Learning as appropriate) for sDNA and cDNA for BENLUX markets e.g., prediction of store potential, store segmentation, assortment optimization, asset deployment, audience profiling, etc. The position exists to unlock competitive advantage in go-to-market and execution through cutting edge data science, advanced analytics, and AI techniques, with a focus on OT, TT and AFH for BENLUX. The focus of this role is ensuring the successful implementation of data science solutions that deliver. The role holder will also leverage best practice to help to establish a reusable data science ecosystem. Qualifications Bachelor's or advanced degree in a quantitative field for instance a Master’s degree or PhD in data science or math (e.g., Computer Science, Mathematics, Statistics, Data Science) or equivalent experience 7 years + of relevant advanced analytics experience in Marketing or Commercial in either Retail, or CPG industries. Other B2C domains can be considered Proven experience with medium to large size data science projects Advanced knowledge of key data science techniques: Combining data from multiple sources through APIs, Semantic Web, etc. Data preparation and feature engineering Supervised / Unsupervised learning Collaborative Filtering Location Analytics & Intelligence Proficiency in programming languages such as Python, R, or SQL. Good to have experience with data processing frameworks (e.g., Hadoop, Spark) Good to have understanding of data engineering principles, data integration, and ETL processes. Excellent problem-solving skills and the ability to translate business needs into data-driven solutions. Strong communication and interpersonal skills, with the ability to effectively convey complex concepts to both technical and non-technical stakeholders.