Data Scientist

• Translate business questions into testable analytical objectives
• Collect, clean, and prepare structured and unstructured data to enable modelling
• Build descriptive and diagnostic insights through exploratory data analysis
• Design production-ready, integrated data science solutions together with solution architects
• Develop and improve solutions using statistical methods, machine learning, deep learning or GenAI when appropriate
• Productionise solutions following MLOps/LLMOps principles (scheduled workflow jobs, containerised APIs or interactive apps)
• Set up monitoring for accuracy, drift, and system performance
• Collaborate with Technology and business teams (Supply Chain, Commerce, Store Operations and Real Estate) to embed solutions into daily operations

• Design predictive and optimization models to improve planning, inventory, and logistics decisions
• Deliver high-impact use cases across sourcing, factory planning, and logistics while contributing to advanced solutions like digital twins
• Build scalable data products such as: dashboards, decision engines, and automated workflows, that enable real-time insights and better decisions
• Work closely with supply chain and commercial teams to turn real-world challenges into practical, data-driven solutions and communicate insights clearly

Data Scientist – Supply Chain

• Own end-to-end ML projects from problem framing through to production deployment, defining approaches, managing trade-offs, and communicating findings to stakeholders
• Produce technical design documents that guide modelling choices, weighing data availability, serving latency, interpretability, and long-term maintainability
• Design and own LLM-based solutions for customer or partner representations, content personalisation, or recommendation, including training data curation and evaluation methodology
• Build evaluation frameworks that link to business metrics, run well-powered online experiments, and investigate offline-to-online performance gaps
• Close the gap between offline model performance and online business impact through rigorous experimentation and measurement
• Act as primary technical reviewer for junior colleagues, providing substantive, teaching-oriented feedback that raises the team's overall standard
• Collaborate with ML Engineers and Data Engineers to ensure models reach meaningful commercial scale across multiple markets
• Shape technical direction across the AI stack, spanning search ranking, homepage personalisation, promotions intelligence, and foundation model applications

Senior Data Scientist

• Improve and test parts of our Transaction Monitoring models using Python (PySpark) and existing code bases.
• Present model outcomes and technical solutions to stakeholders and explain the impact of your work.
• Collaborate with colleagues across squads to develop and implement new features.
• Work on your career by developing and improving transaction monitoring models together with experienced colleagues.
• Contribute to solutions that support the bank in managing financial risks.
• Join a team of around 10 colleagues, including Data Scientists, Product Owners, Scrum Masters and other specialists.

• Lead and manage the Real-World Data team, including RWD Associates, Specialists, and Interns.
• Develop and maintain the overall RWD collection strategy at myTomorrows.
• Oversee the set-up and execution of RWD collection within EAPs and non-interventional studies.
• Support clients on eCRF design, database set-up, and data management planning.
• Author and review RWD-related policies and SOPs within myTomorrows' Quality Management System.
• Represent myTomorrows externally on RWD topics — including at conferences, business development meetings, and audits.
• Build awareness and understanding of RWD internally across the organization.
• Stay current on data collection practices and regulatory developments in the EAP space.

Manager, Real-World Data

• Play a pivotal role in the development and deployment of cutting-edge Gen AI models and solutions
• Responsible for building, testing, and maintaining our Gen AI, RAG and NLP solutions
• Work throughout the whole life cycle of data science projects: design, implementation, production and beyond
• Deliver efficient and production-ready Python code
• Collaborate closely with developers to deploy and productionize our data science pipelines
• Collaborate with subject matter experts in biology and chemistry domains to validate the output
• Data collection, data analysis, model development, defining quality metrics, quality assessment of models and regular presentations to stakeholders
• Create production-ready Python packages for each component of data science pipelines (such as pre-processing and model inference) and their deployment together with software engineering team
• Optimize and customize Retrieval Augmented Generation (RAG) pipelines to meet specific project requirements that involve content ingestion, machine translation, and contextualized information retrieval
• Ingest, preprocess, and transform large-scale multilingual data to ensure high-quality inputs for downstream models
• Build AI agentic models integrated with RAG pipelines
• Conduct rigorous testing and evaluation of AI models to ensure high performance and reliability
• Integrate data science components and perform end-to-end quality assessments
• Maintain robustness of data science pipelines against model drift and ensuring consistent output quality
• Establish reporting processes for pipeline performance and develop automated re-training strategies for existing pipelines
• Collaborate with cross-functional teams to integrate AI solutions into existing products and services
• Lead and manage projects with a team of data scientists and independently execute the entire small-scale projects
• Mentor junior data scientists and foster a knowledge-sharing culture within the team
• Stay up-to-date with the latest advancements in AI, machine learning, and NLP technologies.

• Lead the Master Data Center of Excellence and the Data Migration team
• Partner with transformation programs, Enterprise Architecture, and business stakeholders
• Drive enterprise initiatives across Master Data Management, Data Quality, and Governance
• Support enterprise transformation and ERP-related programs with strong data management practices
• Establish and maintain governance standards, processes, and operational controls
• Drive continuous improvement in data quality monitoring and governance processes
• Collaborate closely with Data Engineering, BI, Architecture, and business teams
• Support implementation of scalable governance and operational frameworks

Data Management Manager

• Design and build machine learning, NLP, and generative AI systems for scientific discovery, knowledge extraction, and intelligent content understanding
• Work with large-scale datasets including scientific publications, research data, knowledge graphs, and metadata
• Apply a range of approaches from classical ML methods to deep learning, embeddings, and LLM-based solutions
• Develop capabilities such as semantic search, information retrieval, entity extraction, classification, and summarization
• Build, evaluate, and improve models that support decision-making and accelerate research outcomes
• Train, fine-tune, prompt, and integrate models into production-ready systems
• Build and maintain scalable data pipelines for experimentation, inference, and monitoring
• Collaborate with cross-functional teams to translate complex challenges into robust technical solutions

Data Scientist III

• Turn real business problems into analytical questions and model designs.
• Work with traders, risk managers, finance, digital, and data teams to understand how decisions are made.
• Build and test machine learning / statistical / optimization models and evaluate them with robust validation approaches.
• Create datasets, explainability outputs, and decision-support products (recommendations, alerts, dashboards, decision cards).
• Communicate results clearly, so non-technical stakeholders can trust and act on them.
• Typical projects include: regime-shift detection, price sensitivity/hedge analytics, forecasting supply/demand/inventory risk, and explainable tools for decision reviews.

Data Science Graduate Program

• Lead development of statistical and machine learning models to optimize performance
• Design and execute A/B/n experiments for measurable improvements
• Translate results into production-ready solutions
• Collaborate with cross-functional stakeholders to identify use cases
• Own the model lifecycle from ideation to performance monitoring
• Present findings to technical and non-technical audiences
• Contribute to data science practices and frameworks

Data Scientist jobs in Netherlands

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Data Scientist

Data Scientist – Supply Chain

Senior Data Scientist

Data Scientist

Manager, Real-World Data

Senior Data Scientist

Data Management Manager

Data Scientist III

Data Science Graduate Program

Data Scientist – Medior