Data Scientist
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Location:
Brussels BE-1 day a week onsite Work with stakeholders to gather requirements, translate business challenges into data-science hypotheses, establish success metrics and develop data-mining / ML solutions.
• Partner with UX and product teams to ensure clear presentation and interpretation of analytical outputs.
• Assess data accuracy; identify, collect, transform and prepare structured and unstructured data, including feature engineering and production-grade data readiness.
• Build statistical models, machine-learning algorithms and scripts for forecasting, classification, segmentation, inference and other business problems.
• Design, train, tune, evaluate and validate predictive models; apply cross-validation, significance testing and controlled experiments such as A/B tests. technical audiences, and document models and cross-system dependencies.
• Operationalise models using MLOps, CI/CD, model registries, reproducible pipelines and production Expertise needed: Python and pandas for advanced analytics, modelling and automation; scikit-learn and Hugging Face Transformers for machine learning and NLP.
• AI assistants and Retrieval-Augmented Generation (RAG): design and optimisation of retrieval pipelines; similarity and vector search, including FAISS.
• AI safety: practical guardrailing and red teaming against hallucinations, bias and prompt injection.
• Explainable and auditable AI/ML: transparency, feature attribution, post-hoc interpretation and traceable model decisions.
• MLOps: CI/CD, model registries, testing, MLflow and Langfuse; model deployment through containers, APIs, serving frameworks and Docker.
• Data storage and retrieval: PostgreSQL, Oracle and Elasticsearch.
• AI governance, GDPR and EU AI Act; bias monitoring, drift detection, model cards and ethical / compliant model design Must Have: ETL/data: SAS Data Integrator, SAP Data Services, dbt, Apache Iceberg, dlt, Python, Java, Oracle, PostgreSQL. Search: Elasticsearch. Reporting: Python, Angular, Qlik Sense, SAP Business Objects. ML infrastructure: Airflow, MLflow, Weaviate, TensorFlow, DVC, AI. Data governance: OpenMetadata. Cloud: AW
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