Senior Data Scientist I

Description

The company is a global information analytics firm serving research, academic and clinical communities. The Data Science group sits within Technology and is growing.

The team builds AI that helps researchers find, understand and act on scientific knowledge. The work covers machine learning, NLP, large language models, retrieval, reasoning workflows and generative AI designed for science.

You will work with large, varied and complex data: publications, raw research datasets, biomedical content, citations, metadata, ontologies, taxonomies, knowledge graphs, multilingual text and domain data across many scientific fields.

As a Senior Data Scientist in Research Intelligence and Knowledge Discovery, you will build and ship AI, machine learning and LLM-based tools for scientific discovery, research intelligence and editorial work. You will cover the full model lifecycle and pick the right technique for each problem, including deep learning, RAG, semantic search, agentic AI and knowledge systems. You will work closely with engineering, product, research and domain experts.

The aim is tools researchers can trust: ones that show evidence, keep context, cite sources, explain their output and save them time.

Responsibilities

• Lead or contribute to AI and data science projects that help people explore, connect and act on complex scientific information.
• Design, build and deploy AI, machine learning, NLP and generative AI systems, such as LLM applications, semantic search, RAG, agentic workflows and knowledge discovery tools.
• Improve research and decision-support features using embeddings, retrieval pipelines, prompt design, knowledge graphs, ontologies, citation networks and domain content.
• Set up evaluation, experimentation and monitoring for AI and search systems, covering relevance, grounding, hallucination detection, accuracy, reliability, scalability, cost and user impact.
• Work with engineering, product and research teams to move AI into production, shape technical direction, mentor other data scientists and promote responsible AI and good practice.
• Work with scientific, scholarly and biomedical sources such as publications, abstracts, full text, datasets, metadata, citations, author networks, affiliations and institutional information.
• Build knowledge and discovery systems that use knowledge graphs, ontologies, taxonomies, semantic enrichment and multilingual content to improve search, retrieval and insight.
• Build generative AI and decision-support features that combine behavioural signals, personalisation and evidence-grounded content into accurate, explainable and measurable experiences.
• Take complex AI and data science work from concept to production, make technical and architecture decisions, design evaluation strategies and influence product direction with a high degree of autonomy.

Requirements

• Substantial experience applying data science, machine learning, AI, NLP, information retrieval and statistics to build scalable production solutions, backed by an advanced degree or equivalent practical expertise in a quantitative field.
• Hands-on experience with modern AI, including machine learning, deep learning, LLMs, generative AI, semantic search, RAG, embeddings, ranking and AI evaluation frameworks.
• Proficiency in Python and common data science tools, with production-quality coding skills, experience with large structured and unstructured datasets, and experience deploying with cloud, software engineering and MLOps practices.
• Strong communication, problem-solving and leadership skills. You can turn complex business needs into scalable AI solutions, guide technical direction, mentor peers and deliver measurable impact.
• Relevant experience may include deep work with LLMs, generative AI, RAG, semantic search, vector retrieval, ranking and agentic AI workflows that produce evidence-grounded results.
• Relevant experience may include NLP and advanced machine learning such as classification, entity extraction, summarisation, machine translation, clustering, transformers, deep learning, transfer learning and other current architectures.
• Relevant experience may include building and evaluating production AI with frameworks for relevance, faithfulness, hallucination detection, reliability and user value, plus knowledge graphs, ontologies, semantic enrichment and responsible AI principles such as transparency, robustness, reproducibility and trust.
• Valuable: strong software engineering across Python, SQL, object-oriented programming, testing, CI/CD, DevOps, APIs, databases and collaborative tools such as Git, Jira, GitHub and GitLab.
• Valuable: hands-on work building and scaling AI/ML systems with Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Databricks, Spark, vector databases, AWS or Azure, MLflow and Kubeflow.
• Valuable: experience building large AI systems with strong experimentation, evaluation, model lifecycle management and performance work such as parallelisation, caching, batching, latency reduction and cost-efficient deployment.
• Valuable: experience with AI-powered knowledge discovery, search, recommendation and conversational systems, such as AI assistants, agentic RAG, citation-aware reasoning and evidence-grounded AI in scientific, scholarly, biomedical or other knowledge-heavy fields.
• Valuable: a record of running production AI with monitoring, drift detection, automated retraining and cost-effective, high-throughput performance in regulated or high-trust settings.
• Valuable: technical leadership shown through research, publications, patents, open-source work or applied AI projects, and interest in AI-assisted development, human-in-the-loop evaluation, responsible AI and next-generation intelligent systems.

Conditions

If based in Amsterdam at the Radarweg office, the base pay range is €53,800 to €89,900.

This role may fall under a Dutch collective labour agreement. Ask the hiring team for details.

The company is an equal-opportunity employer.

Benefits

• Country-specific benefits are available based on your location.
• Benefits may include wellbeing support, flexible working arrangements, shared parental leave, study assistance, sabbaticals and ongoing learning and development.
• Your work helps researchers save time, find evidence, spot patterns, connect ideas and decide better, and makes scientific knowledge easier to find, trust and act on.