> Markdown version of [/jobs/ext/4015156-data-scientist](https://www.wearedevelopers.com/jobs/ext/4015156-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** YER - **Location:** AMSTERDAM ZUIDOOST, Netherlands - **Contract:** Contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Microsoft Azure, Continuous Integration, Graph Database, Python (Programming Language), Machine Learning, Operational Data Store, OpenAI, Cloud Services, Tensorflow, Azure Machine Learning, Software Engineering, LangChain, Retrieval-Augmented Generation, System Availability, Large Language Models, Generative AI, Agentic-AI, Git, Pandas, AI Platforms, Pyspark, Scikit Learn, Kubernetes, Information Technology, AutoGen, Machine Learning Operations, Api Design, Semantic Kernel, Databricks - **Published:** October 30, 2026 - **Apply:** https://www.yer.nl/solliciteren/?vacature-id=22167 ## About the Role * A bachelor's or master's degree in computer science, artificial intelligence, data science or a related discipline, or equivalent professional experience. * Strong Python, PySpark and software-engineering skills, including API development, automated testing, Git and maintainable application design; experience with libraries such as pandas, scikit-learn and TensorFlow. * Knowledge and proven experience using Azure, particularly Azure Databricks, for machine-learning model development and MLOps. * Knowledge and experience with MLOps, including end-to-end data and machine-learning pipelines. * Hands-on experience building generative AI or agentic AI applications and developing modern AI systems such as RAG and agent-based systems. * Experience with LLM APIs and platforms such as Azure OpenAI or equivalent services. * Experience with orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, Pydantic, AutoGen or comparable technologies. * Understanding of prompting, structured outputs, RAG, embeddings, and vector or hybrid search. * Experience designing autonomous workflows, decision logic and safe tool execution, and evaluating AI-generated outputs. * Familiarity with working according to CRISP-DM cycles. * A strong communicator who can translate technical results and ideas for non-technical stakeholders. * Comfort working in exploratory environments where problem definitions and data availability may evolve, and an ability to structure and validate new use cases. * Experience collaborating with colleagues located in different parts of the world. * A security-first mindset, with attention to privacy, authorization, prompt injection, data leakage and responsible AI. * Fluency in English; Dutch is a plus. Nice to have * Experience developing predictive maintenance models. * Knowledge of event-driven architecture, Kafka, knowledge graphs, or time-series and operational data. * Experience with Azure AI services, Databricks AI services or Kubernetes. * Experience evaluating AI systems and designing human-oversight and feedback mechanisms. * Familiarity with industrial automation in warehouse operations. What you bring as a colleague * You are curious about emerging AI technology and pragmatic about turning it into dependable products. You enjoy working across research, software engineering and domain expertise. You challenge assumptions, test solutions with real users and take responsibility for the quality, security and operational performance of what you build. ## Description We are looking for a Data Scientist with experience implementing AI solutions to join the Maintenance Assistance team. You will work at the intersection of data, machine learning and agentic AI in a multidisciplinary, international product team alongside AI and data scientists, software and data engineers, domain experts, architects, UX specialists and product leadership. You will translate operational challenges into reliable AI capabilities that deliver measurable customer value. You will design, develop and productionize solutions that support operational and maintenance decision-making in real-world environments. These systems reason across multiple steps, use tools and operational data, recommend actions and help users solve complex problems. Your work will span concept development and evaluation through secure, scalable production deployment, delivering tangible value to maintenance and operations teams. What you will work on * Agentic assistants that analyze operational situations, retrieve relevant information and recommend actions. * Multi-agent and tool-using workflows that coordinate data, APIs, models and business rules. * Generative AI applications grounded in trusted enterprise and operational data. * AI-enhanced process mining, anomaly investigation and root-cause analysis. * Predictive and decision-support capabilities for process optimization and maintenance. Your responsibilities * Design models and develop algorithms that solve real-world problems, including agentic AI applications with multi-step reasoning, tool use, memory orchestration and human-in-the-loop controls. * Contribute to advanced predictive maintenance solutions and evolve them into systems that support operational and maintenance decision-making. * Analyze operational and asset data; develop and validate models; and translate analytical results into actionable insights and recommendations with domain experts and end users. * Build reusable AI services and APIs that integrate with operational data, digital products and existing cloud services. * Develop retrieval-augmented generation (RAG) and knowledge-grounding solutions using structured data, documents, events and knowledge graphs. * Productize prototypes using sound software-engineering practices, automated testing, CI/CD and observability; support the integration, productization and deployment of models and AI solutions in production environments. * Improve existing solutions and explore new use cases and asset types. Assess data availability and quality, define approaches, validate potential value and iterate hypotheses in uncertain or evolving problem spaces. * Expand predictive capabilities across different systems and components. * Explore developments in generative AI, machine learning, process mining and event knowledge graphs, turn promising research into practical capabilities, and apply relevant developments in data science and AI.